The game-changing possibilities of the cloud

Reviewing India’s journey through cloud transformation and exploring the challenges, hidden potential, and best practices

Atul Gupta
VP – Products

India is no stranger to cloud technologies. But there is still a perceivable lag in its journey towards cloud adoption. One could attribute this sluggishness to multiple factors – from the lack of well-rounded or clearly defined policies to inadequate funding or delayed access to technology breakthroughs in the field. But the COVID-19 pandemic has accelerated India’s push towards cloud transformation in a move that holds immense potential for the country’s business sectors.

Since the dawn of the decade, there has been a relentless focus on integrating innovative technologies into the business landscape. These technologies hold the potential to make business operations more fluid and flexible. Every few decades, the IT industry sees itself at the cusp of a paradigm shift that furthers its ability to rise to new challenges and deliver better outcomes. Cloud transformation constitutes one such instance.

The business potential of IT has increased manifold since the early 2000s. Digital adoption has skyrocketed, resulting in a massive explosion of data. Businesses need improved scalability and lower latency that in turn translates to more agility and faster turnaround time. These demands, in addition to the pursuit of increased competitive sustainability, are driving the market today. In such a competitive scenario, companies that are seriously eyeing a share of the marketplace have two options:

  1. They can power their technology requirements in-house, doing everything from purchasing the IT infrastructure to writing the code, testing, and running it – activities that require tremendous time and effort.
  2. They can buy plug-and-play components, customize them using minimum code and be up and running in a globally compatible ecosystem in virtually no time.

Cloud speaks to scenario #2, where it offers CTOs the potential to innovate and launch ideas faster, with better accuracy and more cost-effectiveness. It becomes a formidable ally for enterprises that wish to maintain a competitive standing in the market.

Cloud technologies pose a multitude of benefits to companies, including the following:

  • The flexible, pay-as-you-go models offered by cloud platforms enable organizations to shift gears from a highly limiting Capex-centric model to an agile Opex model.
  • Spikes in demand and seasonal variations can be catered to with a lower budget, without impacting performance or robustness.
  • Businesses that aspire to go global can scale up within minutes or be up and running almost instantaneously.
  • Component optimization is possible, as every aspect of any cloud platform is already researched thoroughly and reviewed by users across the world.
  • Cloud empowers companies with predicted performance enabled through high levels of observability and transparency.

India and fintech: Head in the cloud

It’s true that when compared with developed economies, India’s journey to cloud adoption is yet to come of age. However, the pandemic has rigorously turned around this lackluster standing. It has steered Indian enterprises in the direction of the cloud, with a reported 60% or more planning to leverage the cloud in the near future, according to IDC.1

But more willingness to onboard cloud does not necessarily imply a clear road ahead. Even at a purely operational level, there are many challenges that Indian industries, especially the public sector, encounter on their journey to cloud, beginning with the basics – the how-tos.

Bold moves or baby steps?

Luckily, these problems have not percolated the roots of India’s privately owned financial services space or its booming fintech sector. Fintech in India comprises some globally renowned private players, a testament to its ambition to evolve into a product innovation hub. In a business-as-usual scenario, cloud offers little or no challenges to such organizations. Nevertheless, fintech and FIs need to be prepared for unprecedented scenarios, particularly given the sensitive nature of their business. These sectors need utmost transparency in how their data and IP are being controlled, where their applications are hosted, the risks involved, and so on. They also need to be certain about the strategic goals behind cloud transformation and should go in with a clear exit strategy in the event that the vendor is unable to deliver to these goals. They need to be wary of vendor lock-ins; these limit the efficiency of their cloud operating model.

A planned foray into cloud computing can deliver fantastic results. We have witnessed this in many cases, including Axis Bank, which made tremendous progress in increasing its customer base and boosting productivity through cloud enablement. Having a cloud transformation committee would help govern the nuances of cloud adoption, including feasibility checks, weighing costs, benefits, and returns, and tracking progress. Going in without a committee may at the very least lead to unnecessary gridlocks and delayed progress.

Conclusion

In the last few years, we have seen a lot of hype around cloud and the benefits it has to offer. In the Indian context, these benefits depend on how effectively we can channelize the cloud operating strategy while involving key stakeholders and dedicated transformation committees and leveraging newer pricing and operating models. Having a clearly articulated operating model and the right skill base are instrumental to getting the cloud strategy right, as is having the keenness and willingness to adopt. Organizations need to be willing to go the extra mile and explore what’s new and exciting on the horizon, while not buckling down under the pressure to circumvent risks. Only then can they realize the full potential of the cloud and the paradigm-shifting benefits it stands to offer.

Authors

Atual Gupta

Atul Gupta

VP – Products

A profound expert in client-facing experience across the Globe in Sustainable Product Innovation, Strategy Development, Digital, Consulting, and Execution. His strong Technical and Functional Experience has led to scaling up team members consisting of Specialists, Architects, Scrum Masters & Storytellers

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Key trends in Model Risk Management in 2021

The Lehman Brothers crisis and the subsequent recession gave rise to a new economic regime. Now, in the next normal, it’s time for yet another regime change. When it comes to charting the strategic growth of financial institutions, model risk management has earned its seat at the table in the banking and financial services industry.

Rajiv Singh
Data Scientist

Prashanth
Product Manager, Risk

The Lehman Brothers crisis and the subsequent recession gave rise to a new economic regime. Now, in the next normal, it’s time for yet another regime change. When it comes to charting the strategic growth of financial institutions, model risk management has earned its seat at the table in the banking and financial services industry.

Our world has gone progressively digital in the last decade or so. In the next normal, the global financial system will see new types of model risks emerge from the woodwork. In a way, this is a wake-up call for us to rise to the challenges posed by newer and unprecedented risks in financial models as well as re-examine our approach to risk analysis.

Why is managing model risks important?

Banks rely on financial models for their routine decisions and tasks. The data explosion, over the last decade, along with the changes in the way we work and deliver, has resulted in these models becoming increasingly complex.

The ERM function of today is entrusted with identifying new opportunities to deliver value in addition to addressing actual and potential threats.

Such a scenario presents obvious reputational risks as well. There are heavy dependencies on financial models for day-to-day functioning and critical decision-making. The repercussions of a malfunctioning model would be catastrophic. We’re taken back to the sub-prime mortgage crisis of 2007-2010 that eventually pulled down several banking giants and cascaded into a worldwide financial downturn. The implications are even more grave in today’s context, where everything is digital and heavily scrutinized.

What is changing in 2021?

As we cross over from a turbulent 2020 into 2021, the assumptions that defined the pre-COVID era will be revisited. As of today – set in the context of remote collaboration – the way financial institutions are doing business, assessing risks, pricing products, and valuing transactions are all changing.

The evolution of the financial services sector in 2021 will be led by these key factors:

  1. Data-driven digital transformation – Next-generation banking systems will use digital platforms, leveraging the internet, IoT, AI/ML, big data, and analytics, for critical decision-making. Data will be king, dictating the future of banking across all aspects related to credit, risk, asset, and investment management.
  2. New entrants – Specialist fintech companies are already revolutionizing the playground through niche and evolved offerings. Futuristic fintech solutions will equip banks and stakeholders with the right suite of tools to address their most pressing issues.
  3. Banking transformation – Led by the transformation in the fintech sector, banking has evolved significantly in the last few years. Innovative concepts, like mobile money and aggregator platforms for P2P lending, are erasing the thin line between banking and technology.
  4. Compliance requirements – There has been a rising demand for more regulatory intervention in the model risk space. Similar guidelines, as we have with OCC, SR 11-7, or TRIM in Europe and the US, could very well influence the Indian regulatory framework.

Key trends in model risk management

As technology unites people and infrastructures across geographies, demographics, and industries, the ability of a model to integrate and manage risks across diverse ecosystems will be definitive. Next-generation financial models need to be able to integrate new information and process it in an agile and transparent way.

In 2021 and beyond, we will see model risk management influenced by a marked increase in analytical capabilities, and breaking developments, such as the IND AS rollout and open banking:

  1. The influence of AI and analytics: AI is already helping leading financial players dramatically boost market growth through competitive insights. In 2021, we will see increased adoption of AI and analytics to make the best use of banking data for improving efficiencies and gaining competitive intelligence.
  2. IND AS 109: With the advent of IND AS 109, computational changes, like the adoption of ECL, are bound to add to the stress placed on India’s frayed banking system. Regulatory authorities are balancing the fine act between the adoption of new financial guidelines without additional strain on banks. As of now, efforts are directed towards resolving fundamental challenges, like NPA, compliance, eligibility, and investment norms, with an added focus on reducing risk exposures.
  3. Open banking: Open banking eliminates banks’ dependencies on external vendors to aggregate crucial customer data. It can make decision-making faster and more efficient. Besides driving the growth of financial models that are a lot more advanced, this will also amplify calls for better banking infrastructure and more diligence in regulations.
  4. Automation: One of the notable differences in model risk management in India’s private and public banks is in the use of validation models. While most private banks are using automated validations, a lot of PSUs are still reliant on manual validations. 2021 may bring fundamental changes to this practice. With an increase in regulations and mandatory compliances, banks will need to turn to automation for model validation and risk management.

Authors

Rajiv Singh

Rajiv Singh

Data Scientist

Mr. Rajiv comes with 15+ years of professional experience working through Model Development and Validation for banks across the globe. The data scientist in him has also contributed to the development and implementation of Integrated Risk Management Solutions. He is an MBA Finance & M.Sc.(Comp Sc.) graduate and is also certified in a couple of other fields as Financial Risk Manager (FRM), BAI-IIM-B and PGP-AL/ML.

Prashanth

Prashanth

Product Manager, Risk

Expert in product management, model risk and financial engineering with experience in large investment banks, asset managers and broker-dealers. He is an engineer and an MBA graduate and also holds CQF from Fitch)

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Enterprise risk management and the banking industry – A 2021 outlook

What does the next normal hold for ERM – a critically indispensable banking function in the post-pandemic era?
With the resurgence of COVID-19 in several parts of the globe, we are currently grappling with the biggest black swan event of our lifetime. The crisis continues to unfold to this day, impacting several industry sectors and businesses of all scales and sizes. Even banks are not an exception to the pangs of the pandemic. In the current precarious and rapidly escalating situation, enterprise risk management has re-emerged as a potent interventional mechanism to effectively alleviate the impact of a crisis.

Kasthuri Rangan Bhaskar
VP, Financial Services Practice & Risk SME (Lead)

What does the next normal hold for ERM – a critically indispensable banking function in the post-pandemic era?

With the resurgence of COVID-19 in several parts of the globe, we are currently grappling with the biggest black swan event of our lifetime. The crisis continues to unfold to this day, impacting several industry sectors and businesses of all scales and sizes. Even banks are not an exception to the pangs of the pandemic. In the current precarious and rapidly escalating situation, enterprise risk management has re-emerged as a potent interventional mechanism to effectively alleviate the impact of a crisis.

The prolonged uncertainty around COVID-19 has changed the perception of risk management as a function. The lines between traditional risk management, crisis management, and resilience are rapidly blurring. ERM in today’s context is highly evolved as a function and its success is rooted in an organization’s ability to foster productive partnerships with key stakeholders. Banks are also heavily investing in technology to enhance their risk management capabilities. They are increasingly using risk intelligence from external sources and data visualization techniques to improve decision-making and risk mitigation, among others.

What has changed and why?

Even after the last 20 years of continual enhancement, risk management is too often mistakenly thought of as a compliance function within banks. But while compliance regimes may work well for known risks with clear implications and proven mitigation strategies in a fairly static environment, the current situation is anything but static. The rise in regulatory activity related to ERM, brought on in part by the pandemic, has put pressure on industries, raising benchmarks and expectations of what constitutes a mature approach to risk management.

The ERM function of today is entrusted with identifying new opportunities to deliver value in addition to addressing actual and potential threats.

Organizational management and stakeholders are realizing that ERM is part of their overall governance process, and internal controls, IT Risk, operational risk, resilience, and so on are its integral parts. The focus now is on honing their risk management capabilities, especially using technology, to enhance monitoring.

Let’s examine this change in the context of India’s banking and financial services sector. Traditionally their focus was on credit and liquidity risk management; operational risk mitigation was limited to capital calculation. Now, the regulatory emphasis has drawn increasing focus to ERM. IT and business continuity risks are today considered a greater threat to banks than credit risk. This has fuelled a more comprehensive outlook towards ERM, in turn impacting the mindset of the CROs.

Emerging trends in ERM in 2021

The Indian banking sector will see an increased focus on effective ERM to collectively reduce risk, accelerate performance, and meet the assurance demands of regulators. Some of the key trends anticipated going into 2021 include:

  1. Reduction in the compartmentalization of the risks: ERM, with its all-encompassing outlook on risk management, would contribute to setting up more comprehensive, robust, and forward-looking ERM frameworks at banks.
  2. More emphasis on IT risk management: While this is bound to happen, there will be added emphasis on proactively managing and monitoring business continuity risk as a separate business risk and not as part of IT risk.
  3. Third-party risk management will get a fresh spin: Not only vendor risk, but by adopting an effective ERM program, banks will be able to perform a forward-looking assessment on IT risk, reputational risks, business continuity risks, and so on.
  4. Quantification of operational/enterprise risks becomes important: This will be significant as metrics alone can determine the extent of risks, the severity, and ultimately the success of mitigation or remediation methods.
  5. Solution adaptability becomes vital to managing risks: The risk management function of banks or any organization can only be effective if it is enabling the business to react promptly and take immediate remedial measures. Technology will prove critical to enabling banks to gain this extent of agility.

ERM landscape in the next normal

How can banks and FIs optimize their risk management teams for a more dynamic environment – that’s the billion-dollar question on everyone’s minds today.

In the current scheme of things, cultivating stakeholder trust would be paramount. This calls for the risk leaders of banks and FIs to think of innovative ways to engage the bank’s diverse stakeholder ecosystem and empower them in decision-making.

It is also critical for regulatory authorities to push for digital transformation in the banking sector. A digitally transformed organization will naturally be more adaptable to the changing business environment and more effective in mitigating risks.

Such a digitally transformed system would be key to increasing stakeholder confidence in a bank’s risk function. After all, informed decision-making requires accurate and timely data. Not only this, it will enable banks and FIs in delivering more relevant information, including predictive information, and in resolving the perennial complexities of keeping up with compliance controls, processes, and reports in response to new mandates.

Authors

Kasturi Rangan Bhaskar

Kasthuri Rangan Bhaskar

Mr. Kasthuri is the Risk SME (Lead) at Bahwan CyberTek with profound experience in Market Risk & Credit Risk, and has over 15 years of experience in the BFSI sector. He has experience working with some of the large mainstream BFSI labels in the country.

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Technology trends shaping Indian fintech in 2021

One of the highest fintech adoption rates, globally. Exceptional levels of banking and mobile penetration. A fast-growing middle-class. Even amid a crippling pandemic, India’s fintech sector is ready for take-off. But, with worldwide disruption impacting businesses, what does the future of India’s fintech look like?

Atul Gupta
VP – Products

One of the highest fintech adoption rates, globally. Exceptional levels of banking and mobile penetration. A fast-growing middle-class. Even amid a crippling pandemic, India’s fintech sector is ready for take-off. But, with worldwide disruption impacting businesses, what does the future of India’s fintech look like?

India is among the fastest-growing fintech markets in the world. The comparatively low cost of data and expanding network coverage has been enabling India’s end-users to accelerate the adoption of financial services.

But not everything about India’s fintech sector is rosy. Developed countries were early movers and they are already global leaders in adopting BFSI best practices and leveraging technology advancements. The endgame in these countries is very different from what it is in India.

While the focus in developed countries is on the adoption of advanced technologies to deliver differentiated and innovative fintech services, in developing countries, like India, the perspective is slightly different.

There are several challenges on the road to India’s digital inclusion:

  • There are perceivable lags in last-mile connectivity.
  • Fintech adoption levels in the lower-income strata are visibly lower.
  • Regulations around security and data privacy are still in nascency.
  • India is yet to tailor a regulatory strategy for fintech that balances convenience with security.
  • Data policies need to be customized more to India’s distinct banking and fintech ecosystem.

Reform-centric endorsements from Indian regulators and positive indicators on the VC front are enabling fintech to move past these challenges. Rapid technological advances, too, have helped accelerate India’s fintech growth.

Six technology trends shaping fintech in 2021

The banks & financial institutions of today are a blend of technology companies specializing in financial enablement. Technology is the glue that holds together its specific stakeholders, assuring cohesive functioning. 2021 will usher in dramatic reformations in India’s banking sector led by:

  1. Hybrid cloud – Hybrid cloud brings businesses the convenience of lower capital investments and unwarranted delays in the set-up. Flexible, pay-as-you-go models, express commissioning, and the certainty that comes with the mixed bag of hybrid cloud will enable companies to remain more fluid and Opex-centric, without spending millions locked in Capex.
  2. Microservices – The emergence of microservices has helped us bid goodbye to large monolithic application structures. This change – to a sleeker, lightweight, and fluid architecture – was driven by the need for applications to become more business-friendly, agile, and outcome-focused. Moving to 2021, we will see microservices giving applications the liberty to go modular and bring in increased agility and reduced time to market.
  3. Blockchain – As a decentralized and immutable source of truth, blockchain will find intensive use in financial services, come 2021. But for discussions to materialize, governments and regulatory agencies will first need to agree on the minimum operational guidelines for blockchain. The convergence of technology with regulations will be a significant challenge to overcome.
  4. Public Cloud – India’s banking system is already making headway in terms of private cloud adoption. On the other hand, public cloud adoption has been slow due to security concerns – one of the key problems curbing the growth of this segment. 2021 will see newer developments on the cloud security front. And, with hybrid cloud gaining momentum, public cloud, too, will see an increase in demand, due to its ability to resolve infrastructural challenges and the last-mile connectivity issues.
  5. Feedback-enabled AI – Private and public sector banks are already using AI & ML for credit monitoring and core banking activities. But, in their current state, AI models are built to function like black boxes, completely opaque to the end-user. Future financial services will be powered by developments in responsible and feedback-enabled AI. In 2021, AI will accelerate its transformation into becoming more ethical and explainable to achieve the objectives of fairness, trust, and transparency.
  6. Open banking APIs – A key use case for APIs in 2021 would be around open banking. Banks, with customer consent, can use APIs to share salient customer and financial data with third parties for use and analysis. When used responsibly, open banking could eventually pave the way to accelerated processing times, shorter go-to-market cycles, improved decision-making, better responsiveness, and have far-reaching consequences on data democratization.

Beating uncertainty with technology

Over the next decade, as AI & deep learning technologies evolve, they will learn to process patterns and historic data more efficiently to arrive at near-perfect predictions – even in highly uncertain scenarios as those involving pandemics.

This will empower socio-economic progress on multiple fronts.

  • Better financial decisions by the governments and regulatory authorities
  • A comprehensive financial services system that is less transactional and more of a way of life
  • Better time and resource management and higher efficiencies through automation
  • More trust in the financial system through transparent and ethical conduct

The only way for us to see foresee unknown risks in the new normal is through the use of advanced technologies. Technology is the answer to not only our present challenges, but it is also the architect of our collective future in the new normal.

Authors

Atul Gupta

VP – Products

A profound expert in client-facing experience across the Globe in Sustainable Product Innovation, Strategy Development, Digital, Consulting, and Execution. His strong Technical and Functional Experience has led to scaling up team members consisting of Specialists, Architects, Scrum Masters & Storytellers

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Credit risk management in the post-pandemic era

The year that was, and key trends in 2021

Credit risk management continues to be critically important across financial institutions. In the last year, the top concerns faced by credit risk professionals has dramatically changed, owing to the economic downturn, COVID-19 pandemic and worldwide disruption. 2020 essentially turned the concept of credit risk management on its head. With perspectives changed forever, what’s to become of credit risk management in 2021?

Shankar Ravichandran
Senior Manager at BCT Digital

The year that was, and key trends in 2021

Credit risk management continues to be critically important across financial institutions. In the last year, the top concerns faced by credit risk professionals has dramatically changed, owing to the economic downturn, COVID-19 pandemic and worldwide disruption. 2020 essentially turned the concept of credit risk management on its head. With perspectives changed forever, what’s to become of credit risk management in 2021?

There are always risks associated with the business of lending. In the normal scheme of things, credit risk is usually assumed on the borrower’s side, and a good lender is one who makes the best assessment of this risk. However, to make an effective assessment, the lender needs to understand all the associated risks completely, including the financial position of the borrower, as well as their projected position based on his or her business or income.

These assessments are becoming increasingly difficult today, due to:

  • Shorter business cycles – Business environment is changing faster than ever before, aided by rapid technological developments.
  • Digital payment transactions – The banking sector opening up to convenience and better user experience has led to newer risks and rising fraud.
  • Newer business models – Next-generation businesses are increasingly difficult for bankers to understand or assess from a risk perspective.

Banks have always had advanced financial models at their disposal to evaluate risks and predict defaults. However, 2020 brought along the COVID-19 pandemic that not only brought businesses to a screeching halt, but made predictions near-impossible through existing models. The amount of financial distress caused by the pandemic called for a newer approach towards financial modelling and credit monitoring.

  • Firstly, a more interconnected global economy means the risk of contagion is higher. Risk management practitioners today need to be cognizant of global events, and also need to understand the impact such happenings may have on their respective portfolios.
  • The regulatory push on IFRS9 and Expected Credit Losses in place of incurred credit losses is intended to strengthen credit discipline in the financial system. The exact point where there is material increase in credit risk will be determined by the credit risk manager, and this impacts the finances of the bank in a big way.
  • The exit of manufacturing majors from China could mean more credit demand from their counterparts in India. The Indian banking system should be geared up to meet this challenge using innovatively structured credit practices with competitive terms.

The effects of the pandemic cannot be easily understood due to its non-linear nature. Some industries, like hospitality and entertainment, are completely in doldrums, others are affected to various degrees, and still a few are unaffected. Interventions by authorities and regulators, in terms of suspension of NPA norms and moratoriums, though necessary, have further added to the uncertainty.

Some of the immediate measures taken by authorities, include:

  • The RBI’s sector-wise resolution framework, based on the K.V. Kamath Committee, to mitigate the impact of the pandemic on borrowers
  • Restructuring the framework for MSMEs without downgrading asset classification
  • Emergency Credit Line Guarantee Scheme 1.0 and 2.0, in the journey towards Atmanirbhar Bharat, for providing credit to identified sectors with protection to lenders in the form of guarantees
  • Support to liquidity-starved NBFCs through a Special Purpose Vehicle to purchase short-term papers

While these measures have been helpful in letting borrowers tide over the crisis, the actual quality of the books remains to be gauged. The RBI predicts a severe worsening of asset quality across the system this year. Gross NPA levels of scheduled commercial banks are predicted to worsen – from 7.5% as of Sep 2020, to between 13.5% and 14.8% by Sep 2021.

Source: RBI FSR dated 11 Jan 21

Banking trends of 2021 and their implications on credit risk

The banks of tomorrow will prioritize customer convenience in a big way. Going digital is the first step towards achieving this.

  • In the future, going digital will allow customers to easily perform certain operational activities efficiently and securely – eliminating the need for physical presence. For instance, a digital signature can completely negate the need for physical presence of customers in transactions.
  • Further developments, like Open Banking, will interconnect all financial intermediaries so that authorised data sharing can be done for mutual benefit. For example, a loan aggregator may connect via open APIs to the HRM Systems of companies, the income tax department, and so on, to effectively assess loan eligibility in real time.
  • The focus will shift to cashflow-based lending, in place of balance-sheet- or asset-based lending, as cashflows can be measured faster than assets.
  • All of this will in turn increase the load on banks in terms of monitoring, surveillance and risk management.
    • Greater digitization of borrower data would mean tighter KYC, AML and other formalized checks based on advanced algorithms.
    • Centralisation of credit approval processes leaves less room for subjectivity, and greater dependency on rule-based and data-driven credit policies.
    • To tackle unprecedented risks, self-learning AI/ML financial models will drive risk management, rendering traditional models useless.

The way things are headed, the future of banking belongs to the tech savvy. Dedicated investments in the right technologies will go a long way in helping lenders beat competition. It is important that regulatory bodies give ample guidelines to lenders on digital transformation in credit risk management. In India, the RBI and Finance Ministry have, over the last few years, been proactive in guiding banks in monitoring their credit portfolios.

This sort of intervention is strongly called for, given the compounding effects of the pandemic on an already fragile financial system. From the circular on RFAs in 2015 to guidelines on monitoring parameters issued by the Department of Financial Services, the push towards fintech- and regtech-enabled digital transformation has been strong. These measures will be a strong contributors in uplifting India’s banking sector from its current precarious position.

Authors

Shankar Ravichandran

Senior Manager at BCT Digital

His profound expertise in the field of corporate and retail banking spanning across Credit Risk, Transaction Banking, Service Delivery and Product Management is close to decade. He is an MBA graduate from Indian Institute of Management, Bangalore.

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Staying sure-footed in the new normal

Credit monitoring and preventive intervention with real-time Early Warning Systems

The large-scale disruption brought on by the new normal calls for financial institutions to exercise more caution when it comes to managing their lending activities. Technology-aided solutions, like the real-time Early Warning System developed by BCT Digital, are changing the game – by detecting systemic flaws and vulnerabilities, and upholding the sanctity of the credit management process. On a macro scale, these systems will be instrumental for FIs to build the systemic stability and competitive advantage needed to thrive in the new normal.

Kasthuri Rangan Bhaskar
VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Shankar Ravichandran
Senior Manager at BCT Digital

Ashish Jajodia
CFA Senior Consultant, Risk management at BCT Digital

Credit monitoring and preventive intervention with real-time Early Warning Systems

The large-scale disruption brought on by the new normal calls for financial institutions to exercise more caution when it comes to managing their lending activities. Technology-aided solutions, like the real-time Early Warning System developed by BCT Digital, are changing the game – by detecting systemic flaws and vulnerabilities, and upholding the sanctity of the credit management process. On a macro scale, these systems will be instrumental for FIs to build the systemic stability and competitive advantage needed to thrive in the new normal.

Credit monitoring is an integral part of the lending activity of financial institutions. These organizations have a great responsibility for not only extending timely credit but also maintaining asset quality through continuous monitoring and recovering dues in time. Traditionally, adequate precautions are taken during the assessment and sanction of a loan, however, a lender has to be much more vigilant during the lifetime of the loan and ensure its full recovery.

To achieve this, proactive credit portfolio monitoring must be in place rather than just a reactive approach. Early Warning Systems are technology-led solutions that enable proactive credit monitoring by generating early warning signals of incipient stress or credit fraud events. Warning signals on critical credit events need to be generated at the earliest possible occasion, and timely corrective/preventive measures should be taken before irreversible damages are sustained by the bank on account of any slippage in credit exposure.

Technology has enabled customers to operate their loan accounts with the click of a button from anywhere in the globe, enabling funds to be moved out of the account without much effort. In this scenario, what is needed is not just a detective system that unearths problems after they have occurred (such as by reporting that bank funds availed by a company have been transferred to overseas companies), but rather a preventive EWS system that forewarns bankers of suspicious transactions and enables them to stop such transactions in real-time before they are completed.

The above shift towards electronic payment systems has brought with it challenges as well as opportunities:

  • On one hand, the velocity of transactions has increased, thereby making funds siphoning easier and traceability of funds by bankers and authorities all the more difficult.
  • However, on the other hand, digital transactions leave a record, which can be drilled down to minute detail with the technology available today, on a real-time basis.

A real-time EWS typically involves the following:

  • Integration with source systems in a real-time mode: In a bank, there are various financial and non-financial transactional systems; core banking system, SWIFT messaging, and RTGS/NEFT systems are prominent amongst them. For real-time EWS, seamless integration with the source systems is a pre-requisite. This facilitates all transactional details to flow from the source systems to the EWS system.
  • Processing of all transactions in real-time: A real-time EWS system is capable of scanning all inward and outward transactions on a real-time basis, from the point of transaction initiation by the customer to the stage of completion (acceptance/rejection of the transaction).
  • Scrutiny of each transaction based on pre-set rules in the EWS rule engine:  All transactions are subjected to a filtering process using rules appropriately selected from a library of pre-set rules configured to detect fraudulent and suspicious transactions, much before they are posted into the CBS.
  • Reverse feedback to source systems: Once processing is done by the real-time EWS, along with an alert within the EWS system for records and future tracking purposes, feedback is sent back to the source system which originated the transaction. Illustrative feedback could be in the form of:
  •  
      • Allowing the transaction to pass through without any warning, if all transaction parameters are fully compliant with the applied criteria and rules
      • Allowing the transaction to pass through, but with a warning, in case something suspicious is detected
      • Provision for manual intervention by the bank staff for applying their expert judgment

Some of the major transaction types that can be monitored on a real-time basis include:

  • RTGS/NEFT/IMPS transactions
  • Cheque clearing – inward, outward
  • SWIFT/SFMS messages
  • Intra bank transfer

Conclusion

While traditional Early Warning Systems ensure banks are alerted of signs of stress in borrower accounts months before they turn defaulters, fraudsters operate at a much faster speed – with round the clock internet-enabled transactions and a greater amount of global trade across countries. Hence a real-time EWS that can prevent suspicious transactions instead of merely reporting them is the need of the hour.

Click here for demo

Authors

Kasthuri Rangan Bhaskar

VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Mr. Kasthuri is the Risk SME (Lead) at Bahwan CyberTek with profound experience in Market Risk & Credit Risk, and has over 15 years of experience in the BFSI sector. He has experience working with some of the large mainstream BFSI labels in the country.

Shankar Ravichandran

Senior Manager at BCT Digital

His profound expertise in the field of corporate and retail banking spanning across Credit Risk, Transaction Banking, Service Delivery and Product Management is close to decade. He is an MBA graduate from Indian Institute of Management, Bangalore.

Ashish Jajodia

CFA Senior Consultant, Risk management at BCT Digital

With 12+ years of experience across multiple industries, he has profound expertise in the field of Risk Analytics, Predictive Analytics, Credit Appraisal, Hedging, Project Financing, Financial Planning and Business Development Strategy. He holds a CFA Charter and is an MBA graduate.

 

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AI-based Models and Model Risk
aibased, Thought Leadership
By rt360 January 13, 2020

AI-based Models and Model Risk

AI-based models are more susceptible to certain risks than their conventional counterparts, unless they are put through proper governance and oversight mechanisms. We will look at some of the model risks AI models may be susceptible (over its conventional counterparts).

Kasthuri Rangan Bhaskar
VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Read More

Machine Learning and Credit Fraud Detection
machinelearning, Thought Leadership
By rt360 December 23, 2019

Machine Learning and Credit Fraud Detection

Machine-Learning-based algorithms lend themselves as useful tools for analysing the possibilities of credit fraud. This is typically done by running the algorithms on various transactional data and analysing for hidden patterns.

Kasthuri Rangan Bhaskar
VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Read More

Continue reading “Staying sure-footed in the new normal”

Making big leaps in Banking with Big Data

The banking risk management scenario is steadily evolving. The system of compiling data from information silos and feeding them into manual spreadsheets is now fading into the past.

Ramkumar Iyer
Lead Technical Architect at BCT Digital

Rajiv Singh
Principal Consultant at BCT Digital

The banking risk management scenario is steadily evolving. The system of compiling data from information silos and feeding them into manual spreadsheets is now fading into the past. Claiming its place is a dynamic ecosystem teeming with petabytes of information, collated in real-time from hyper-connected networks and sources. And at the epicenter of this set-up is big data — forever interpreting, analyzing and enabling transformation.

Recent banking reforms are forcing the industry to relinquish age-old techniques of data analysis and modelling, and adopt more rewarding technologies. With digitization, the volume of data has been increasing in leaps and bounds. It is becoming evident that the technology available with most banks is not adequate enough to process all of this data being generated at an unprecedented pace. Not only is an alarming amount of data going to waste, along with it, valuable insights are being lost to the industry.

Enabling the paradigm shift

Luckily, technology advances have facilitated the creation of tools that could handle previously unimaginable amounts of data. There is no longer a dependence on data that is “clean” and computationally manageable. Thus, banking industry is adopting such new and emerging technologies — like big data, AI/ML and analytics

  • Making sense of unstructured data: All around us, massive volumes of data are being generated every day. Most of this is unstructured data — an assortment of different data formats, structures and types. Traditional risk management practices that rely heavily on structured data are rendered inefficient. Using advanced technologies like big data, data analysts can make order out of the chaos and extract critical insights.
  • Single source of truth: The influx of data from various touch points — the news, social media and so on — begs the question of data duplication. Big data products allow large volumes of data to be stored at a single repository, open to query and analysis by different departments within the same organization.
  • Customer-centricity: Traditional banking practices relied on small data sets that were customized to specific data models. Big data can go beyond the restrictions of such information silos, pulling data from multiple channels. This enables layered perspectives and 360-degree analytics, and is immensely valuable in areas like customer profiling for risk mitigation.

Rise of the private banking sector

When it comes to big data adoption, emerging economies like India are yet to pick up pace. The current regulatory landscape being unfavourable to data storage, privacy and access are some of the reasons. In India’s public sector banks, for example, big data adoption is still a work in progress.

In stark contrast, private players were quick to catch up, rolling out new products and initiatives at par with even some of the global counterparts:

  • Self Service BI: As we engineer systems that learn from experience, fraud risk management takes on a new perspective. Deep-learning systems can identify patterns in ways that cannot be coded through rule-based algorithms while detecting potential risks faster than any human expert.
  • Integration of big data with Blockchain: As a universal and immutable source of truth, Blockchain is an authority in curbing financial frauds. The verifiable nature of Blockchain data simplifies analysis. When combined with big data analytics, permission-based Blockchain networks could become immensely valuable as the future of risk management.
  • Tackling cyber risks: Digitization has opened the banking system to online threats. Banks, in turn, are eyeing big-data-based security tools to curb sophisticated cyber threats. Big data can be used to flag systemic security gaps, detect intruder attacks, and launch defensive maneuvers and so on — serving as a highly effective threat monitoring and mitigation mechanism.

Indian PSBs: Roadblocks ahead

Despite the obvious advantage of big data and related technologies, few of the PSBs are embracing this advantage, while there are many others in progress. The push from regulators to roll out advanced technologies into everyday banking is also fairly recent.

Additionally, from a governance perspective, banks are working towards imbibing good data management practices. This includes understanding technologies and their application, managing multi-level integrations, and ushering in the transition. Experienced service providers can help banks with the right advisory support, overcome barriers, and foster effective changes in the organizational hierarchies.

Technology partners must take additional effort to help banks with the nuances of new and emerging technologies. They must be candid in detailing the pros and cons of big data adoption, and act as thought leaders in overcoming industry-specific challenges. Such transparency — which is at the core of all professional relationships — will be hugely helpful in promoting innovation.

Big data, big questions

With the glory, come the questions. The advent of big data raises concerns of data security and authenticity. There is a growing fear that technology is being used for surveillance, and manipulated to serve unjustified purposes.

The below troika raises pertinent questions on the industry’s move to big data analytics:

  • Volume: With the impending data explosion, can the volume and variety of data (the curse of dimensionality) become too much for big data systems to handle effectively?
  • Veracity: As most big data and AI algorithms are black boxes, can regulators really determine their fairness?
  • Volatility: Can all analytical data be retained indefinitely, and if it cannot, will this impact the results of the analytics?

How much of this concern is real? Are users today really in control of their data?

As with all technology, the transformative power of big data and AI needs to be taken with a pinch of salt. When used prudently, big data is revolutionary in its ability to solve specialized problems. It can not only analyse data with remarkable speed, but also discover solutions to problems that would take the average human intelligence years to solve. With this superpower of an ally at their disposal, manual efforts can be focused where they matter most — in strategizing and driving growth. Therein lies the power of big data.

Secondly, every country follows certain set standards for security and privacy. The fundamental tenets of data regulation prevent big data and AI algorithms from discriminating (fairness) or revealing identifiable information. Meanwhile, pre-set techniques analyse data without revealing unique information. Stringent regulations like that of EU’s General Data Protection Regulation (GDPR) enforce this practice.

And last, every technology deployment is rooted in the belief that if caution and prudency are compromised, things are bound to go wrong. Right now, banks, including PSBs, are steadily moving towards customer centricity. Proactive customer service with quick turnarounds has become the need of the hour. For PSBs to enter and beat private players at their own game, a paradigm shift is needed in the status quo. And it takes the right technology and the right service provider to make this shift happen.

rt360 — leading the next generation of banking technologies

The next-wave of FinTech applications led by experienced service providers can make a world of difference. One such example is rt360, built by the bankers-turned-solution-architects of BCT Digital — Bahwan CyberTek. It is a powerful product underpinned by a “Business First, Technology Next” approach, that helps manage, monitor and track models throughout their life cycle.

The industry and technology experts at BCT begin every engagement with the “discovery phase”. The as-is system landscape and processes of a bank are assessed and evaluated at this phase to broaden our understanding of the operations. Every custom development is engineered by taking account of global best practices, industry regulations and our own industry expertise. This accelerates the deployment process while reducing errors and re-work, keeping mission-critical processes afloat.

Some of the proprietary technology innovations fuelled by rt360 include:

  • Early Warning Systems to predict credit defaults through advanced algorithms and rules engine-based analytical set-up
  • Text miner tool using Stanford NLP (Natural Language Processing) to extract critical information from high-volume data pools, and issue alerts
  • Internet scanner to report relevant customer information from online data sources using fuzzy logic; serves as an effective tool for data aggregation, asset and fraud/risk management
  • An incremental upgrade architecture along with an aggregator to prevent duplicate data fetching. The architecture involves requesting only the changed entities and areas of interest on a regular basis (using JSON). This optimizes the network and storage times.
  • Mapping of JSON data to relational storage structures done in multiple innovative ways, without using a native JSON database due to commercial considerations
  • Usage of Apache UIMA Ruta annotation scripts to detect Stock Audit Format
  • Network analysis and inter-linkages of cross directorships (to depict the interconnected nature of firms) using graphical databases

rt360 saves users time and cost by continuously updating the product with new compliance mandates that come from the global regulators. It is presently deployed at eight of the top ten banks in India, helping them to meet their risk management and compliance mandates.

Authors

Mr. Ramkumar Iyer

Lead Technical Architect at BCT Digital

Mr. Ram is a full stack Product Architect with around 20 years of experience in software engineering and product development. His areas of expertise spreads across Product Engineering, Solution Architecture, and PreSales. Technologies he has worked on include Java/JEE, SOA/Microservices, Python, AngularJS, ReactJS, API design(SOAP/REST), Cloud (AWS, GCP), Mobility, AI/NLP and Open source software with premier telecom and banking clients in Agile mode.

Mr. Rajiv Singh

Principal Consultant at BCT Digital

Mr. Rajiv comes with 15+ years of professional experience working through Model Development and Validation for banks across the globe. The data scientist in him has also contributed to the development and implementation of Integrated Risk Management Solutions. He is an MBA Finance & M.Sc.(Comp Sc.) graduate and is also certified in a couple of other fields as Financial Risk Manager (FRM), BAI-IIM-B and PGP-AL/ML.

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AI-based Models and Model Risk
aibased, Thought Leadership
By rt360 January 13, 2020

AI-based Models and Model Risk

AI-based models are more susceptible to certain risks than their conventional counterparts, unless they are put through proper governance and oversight mechanisms. We will look at some of the model risks AI models may be susceptible (over its conventional counterparts).

Kasthuri Rangan Bhaskar
VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Read More

Machine Learning and Credit Fraud Detection
machinelearning, Thought Leadership
By rt360 December 23, 2019

Machine Learning and Credit Fraud Detection

Machine-Learning-based algorithms lend themselves as useful tools for analysing the possibilities of credit fraud. This is typically done by running the algorithms on various transactional data and analysing for hidden patterns.

Kasthuri Rangan Bhaskar
VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Read More

Continue reading “Making big leaps in Banking with Big Data”

rt360 and the Risk-Adjusted Return on Capital (RAROC) calculator

The struggle that banks face in combating credit risks is multi-dimensional. Foremost among them is determining the right capital allocation and pricing for different sources of their revenue.

Shankar Ravichandran
Senior Manager at BCT Digital

The struggle that banks face in combating credit risks is multi-dimensional. Foremost among them is determining the right capital allocation and pricing for different sources of their revenue. These include the bank’s loan commitments, revolving lines of credit (which have no maturity), secured vs unsecured lending, and so on. Balancing the risk and profitability of each transaction is also an area of opportunity to optimize. Banks need to be more prudent than ever, and reliant on advanced technologies in order to maintain asset quality, ensure profitability and deliver a competitive market performance, RAROC.

It goes without saying that good credit management practices are bound to have a resounding impact on the financial health of banks. For decades,, RAROC (Risk Adjusted Return on Capital) and EVA (Economic Value Added) have been globally acknowledged as two of the foremost banking performance evaluators and profitability-measurement frameworks, by way of promoting efficient capital allocation and risk management practices. RAROC, especially, has a key role to play in bolstering the profitability by factoring in the risk quotient of a project or business unit.

In good science, Central Banks across the globe, in line with the guidelines set by the Basel Committee, have been advising that banks adopt a pricing mechanism that computes in RAROC, to temper their risk exposure levels. However, the lack of a standardized methodology for accurate RAROC computation is of concern today and continues to plague credit pricing, especially on the corporate lending side.

RAROC calculator

As a FinTech specialist and pioneer in financial risk management solutions, BCT Digital, a division of Bahwan CyberTek (BCT) helps banks and financial institutions mitigate risk and safeguard operations through its flagship product suite rt360. Built by bankers-turned-solution-architects, rt360’s powerful risk-based pricing solution combines state-of-the-art technology with deep risk management domain expertise to arrive at a profitable yet compelling credit pricing for banks and their customers.

The tool can aid bankers with information on three mission-critical fronts:

  • Pricing: Arriving at the ideal loan pricing and interest mark-up based on the risk-quotient of a loan seeker, weighed against the optimum RAROC threshold set by the bank
  • Credit decisions: Determining the feasibility of loan applications by taking into account the loan seeker’s overall credit rating, transactional history with the bank, as well as other key parameters related to loan purpose and facility. Ability to accept or reject the application by gauging the overall risk vs profitability
  • Collateral coverage: Analytics to decide on the appropriate collateral or guarantee cover for risk mitigation, once the credit-worthiness of the loan seeker has been established
  • Comparability: Getting a 360-degree view of the customer’s engagement with the bank, comparing customer portfolios, profiles, credit worthiness and operational scenarios leading up to lending decisions

https://www.mckinsey.com/~/media/mckinsey/dotcom/client_service/risk/working%20papers/24_the_use_of_economic_capital.ashx

books.google.co.in

A real-world example

Relationship managers routinely connect with commercial loan seekers to discuss loan requirements. The information gathered during the discussion should ideally enable a manager to decide upon the feasibility of a credit deal and arrive at a rational pricing quote.

However, the challenge is the time delay in arriving at an optimal pricing on the fly. This often leads to undesirable pricing trade-offs, with quotes that are at times on the higher end (resulting in the loss of a customer) or lower-than-optimal pricing (leading to revenue leakage on the part of the bank).

The rt360 risk-based pricing solution is a web-based tool that formally computes the optimal pricing or lending rate by leveraging sophisticated algorithms incorporating complex credit risk parameters. Its powerful computation engine and mobile-responsive design make it an ideal tool for bankers on the go. The solution essentially feeds off simple data input, and extensively functions in the background to provide accurate, on-the-spot credit pricing.

Working with the rt360 risk-based pricing solution is a simple, three-step process:

  • Data ingestion: Bankers feed inputs like customer/facility data, utilization, pricing, rating and security into a user-friendly web page.
  • Computation: rt360’s powerful engine computes risk-weighted assets, capital and risk-adjusted returns.
  • Presentation: Usable information, including the RAROC percentage, is displayed along with customizable dashboards and reports — like customer-wise profitability reports, growth reports and more — segregated as per user rights, roles and privileges.

Product highlights

Calculate RAROC || Assess break even pricing || Perform revenue analysis || Manage risks

Holistic credit profile based pricing mechanism: Enables bankers to gain a comprehensive view of the customer’s profitability. For example, the tool takes into account collateral/guarantee for computing losses, resulting in a holistic assessment of risk instead of relying solely on credit rating.

Budgeting mechanism: Assists the business team in yearly/quarterly account planning exercise to systematically determine sales targets for relationship managers.

Access controlled, workflow-based system: Ensures the sanctity of the information entered and mitigate the risk of manual errors

Something for everyone

There are immediate to long-term benefits of adopting the rt360 risk-based pricing solution into your banking system. Relationship managers can optimize the proposed interest rates and fees to arrive at a RAROC percentage that is above the internal threshold set by the bank, yet acceptable to the customer. In addition, risk and product managers can slice and dice varied data inputs pertaining to risk and profitability to achieve granular insights for meaningful decisions and action. Managers can assess customer portfolios, evaluate profitability (segment-wise) and facilitate the risk department staff to conduct sensitivity analysis.

Beyond accurately pricing advances, this customized solution also helps in:

  • Providing a personalized customer experience through customer level aggregation of relationships rather than having a narrow view of account/facility level profitability
  • Facilitating superior efficiency and reduced cycle time for processing credit applications

Authors

Shankar Ravichandran

Senior Manager at BCT Digital

His profound expertise in the field of corporate and retail banking spanning across Credit Risk, Transaction Banking, Service Delivery and Product Management is close to decade. He is an MBA graduate from Indian Institute of Management, Bangalore.

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Measures to maintain liquidity and asset quality through good governance
assetquality, Thought Leadership
By rt360 December 23, 2019

Measures to maintain liquidity and asset quality through good governance

Many NBFCs are structured in a manner that leaves room for inherent asset-liability mismatch. If we take the example of infrastructure HFCs, usually the assets are long-term, while the funding is short term.

Dr. Jaya Vaidhyanathan
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Managing credit risk in a volatile financial market with Early Warning Systems
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By rt360 December 18, 2019

Managing credit risk in a volatile financial market with Early Warning Systems

Collaborating, brainstorming, improvising and iterating without boundaries. It’s every innovator’s dream. For the Indian banking industry, this dream has finally arrived – heralding an era of extraordinary change and progress

Dr.Jaya Vaidhyanathan
CEO, BCT Digital
Shankar Ravichandran
Senior Manager at BCT Digital

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Continue reading “rt360 and the Risk-Adjusted Return on Capital (RAROC) calculator”

AI-based Models and Model Risk

AI-based models are more susceptible to certain risks than their conventional counterparts, unless they are put through proper governance and oversight mechanisms. We will look at some of the model risks AI models may be susceptible (over its conventional counterparts).

Kasthuri Rangan Bhaskar
VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

AI-based models are more susceptible to certain risks than their conventional counterparts, unless they are put through proper governance and oversight mechanisms. We will look at some of the model risks AI models may be susceptible (over its conventional counterparts).

Model Risk in AI Models

Bias Risk: This happens when the results of an ML model are skewed in favour of or against a particular cross section of the population. This may have happened due to various reasons:

  • The training data selected may not have been representative enough, either intentionally or unintentionally
  • The fundamental characteristics of the universe have changed since the model was last trained (for example, income distribution used in the training data has undergone a dramatic change)
  • In certain cases, interaction among the variables may result in bias that is not readily noticeable

A classic example of model bias, which has become public in the recent times, albeit for AI model based recruiting, is this:
https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

‘Black-Box’ Risk: This may occur where highly complex models are used and the relationship between the output and the causal factors are not explainable to common business users, resulting in the ML models turning into a ‘black box’. This is particularly common in areas where vendor models are used. Wherever models turn into black boxes, the users become detached from them. Everything then follows ‘the model says so’ approach, rather than allowing the owners/users to apply expert judgment or discrimination to complement the results. The suitability and appropriateness of the model being used become difficult to evaluate in such cases due to the opacity. Also, by this approach, there cannot be effective feedback from the model owners/users back to the model development team.

This also poses challenges to regulators in validating the models. Read more:https://www.centralbanking.com/central-banks/financial-stability/micro-prudential/3504951/black-box-models-present-challenges-us-regulators

Regulatory/Legal Risk: Usage of ML models, unless subject to tight governance and oversight processes may result in legal risks. This is typically the outcome of other risks such as bias risk or ‘black box’ risk. A classic example is when Facebook was sued by the US Department of Housing and Urban Development for using tools that discriminated certain sections of society in housing-related advertisements.https://www.theverge.com/2019/3/28/18285178/facebook-hud-lawsuit-fair-housing-discrimination

A detailed article on the topic can be found here:https://www.americanbar.org/groups/business_law/publications/committee_newsletters/banking/2019/201904/fa_4/

Technology Risk: Some of the regulators have sounded alarm on the threat of AI algorithms or data being hijacked by criminals. The fear stems from the fact that some of the facets of the algorithm may not be explainable by intuition or by an expert, providing a chink in the armour for cyber criminals to manipulate the data or the algorithm to skew results in their favour. Australian Prudential Regulation Authority Executive Board Member Geoff Summerhayes sounded a warning on this sometime back:https://www.insurancebusinessmag.com/au/news/breaking-news/apra-leader-sounds-alarm-on-ai-use-96353.aspx

To conclude, AI-based algorithms have to be subjected to human oversight and discretion, lest they have unintended consequences. They have to be aligned to an institution’s Model Risk Management Framework, about which we will discuss in the next part.

Authors

Kasthuri Rangan Bhaskar

VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Mr. Kasthuri is the Risk SME (Lead) at Bahwan CyberTek with profound experience in Market Risk & Credit Risk, and has over 15 years of experience in the BFSI sector. He has experience working with some of the large mainstream BFSI labels in the country.

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Key trends in Model Risk Management in 2021
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Machine Learning and Credit Fraud Detection

Machine-Learning-based algorithms lend themselves as useful tools for analysing the possibilities of credit fraud. This is typically done by running the algorithms on various transactional data and analysing for hidden patterns.

Kasthuri Rangan Bhaskar
VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Machine-Learning-based algorithms lend themselves as useful tools for analysing the possibilities of credit fraud. This is typically done by running the algorithms on various transactional data and analysing for hidden patterns.

How it differs from conventional techniques

Conventional statistical techniques for analysing frauds, such as regression analysis, assumes not only the existence of a particular pattern/relationship in the development data but also makes the assumption that such patterns/relationships would stay constant. In reality, such patterns/relationships have drifts. Drifts may have been induced artificially by a fraud perpetrator to avoid detection (E.g. Series of transactions involving small amounts of money, but adding up to a considerable amount) or may have crept in due to the nature of transactions (E.g. On account of seasonal fluctuations).

ML-based algorithms can be used to detect and flag suspect patterns. Such suspect transactions can be subjected to further investigations and courses of action. Extending the argument, the algorithms can be used to reduce false positives as well. For example, seasonal fluctuations in a series of transactions can be flagged separately and allowed to pass (if genuine).

Use Cases

ML techniques can be intelligently applied to a variety of use cases, particularly in the case of frauds:

  • Fund Diversion: Machine Learning can be used to detect any unusual patterns pointing towards possible cases of diversion of fund
    • Are funds being routed to individuals or groups of individuals on a regular basis?
    • Are funds being transferred to third parties on pre-set dates (say, the 2nd fortnight of every month)?
    • Are fixed amounts getting transferred? Alternatively, are amounts being broken down into smaller chunks and transferred, to avoid detection?
    • A combination of the above situations
  • Transactions with Blacklisted Parties: Transactions with parties who are internally or externally blacklisted (E.g. present in the AML watch list). ML-based algorithms can be used to monitor any transactions with such parties.
  • End Use Monitoring: Are the funds being transferred totally unconnected to the lines of business of the borrower?
  • Network Analysis: Is the beneficiary indirectly related to the borrower? (E.g. Beneficiary is one of the common directors)

With electronic transactions surging, it would be impossible for banks and other financial institutions to keep a tab on transactions manually. ML-based algorithms, working on banks’ data, can provide an effective way to keep fraudulent transactions under control.

Authors

Kasthuri Rangan Bhaskar

VP, Financial Services Practice & Risk SME (Lead) at BCT Digital

Mr. Kasthuri is the Risk SME (Lead) at Bahwan CyberTek with profound experience in Market Risk & Credit Risk, and has over 15 years of experience in the BFSI sector. He has experience working with some of the large mainstream BFSI labels in the country.

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