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.

Share On

Also Read

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

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.

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.

Share On

Also Read

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
President — BFSI & Strategic Initiatives and CEO, BCT Digital

Read More

managingcredit, Thought Leadership
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

Read More

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.

Share On

Also Read

Thought Leadership
By rt360 January 13, 2020

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

Read More
Thought Leadership
By rt360 January 13, 2020

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

Read More

Continue reading “AI-based Models and Model Risk”

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: ML 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.

Share On

Also Read

Thought Leadership
By rt360 January 13, 2020

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

Read More
Thought Leadership
By rt360 January 13, 2020

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

Read More

Continue reading “Machine Learning and Credit Fraud Detection”

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
President — BFSI & Strategic Initiatives and CEO, BCT Digital

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. The onus lies on the system to ensure that the cash flow generated out of the assets is both viable and timely. The same wisdom held true in the IL&FS crisis of 2018. The sheer nature of the business of IL&FS demanded effective management of assets and liquidity in the short- and long-terms.

Interestingly, in the case of IL&FS, even though there were startling discrepancies in the balance sheet, auditors and rating agencies noted them and moved on – or arguably, did not take notice at all.
Any stringent regulatory guidelines aimed at improving the liquidity situation of NBFCs were in the transition phase.

In this context, the role of a robust monitoring mechanism becomes most important for safeguarding the health of the system. In retrospect, in the IL&FS scenario, adhering to a four-tier governance framework – involving both internal and external lines of defence – could have perhaps accelerated intervention and remediation by authorities.

More power to RBI through the ratification of new regulations

Perhaps the silver lining – a direct consequence of the NBFC liquidity crisis – was the much-needed shakedown of the shadow banking system jointly by RBI and SEBI. Steps towards remediation included: grouping HFCs under the RBI ambit (pulling them away the National Housing Bank); stripping several non-compliant NBFCs off their licenses1; granting the RBI power over NBFC boards (Union budget FY20)2; new norms to improve the liquidity situation, and so on.

Come 2020, the extension of a gradually scalable LCR and NSFR from banks to NBFCs will also be a strong step by the RBI in this direction.

Liquidity powered by an advanced technology suite like rt360

The liquidity risk management framework of a bank or an NBFC is a decisive factor in how effectively the liquidity position of a financial institution is measured and maintained. The best outcomes are when stakeholders from different levels of the NBFC (organizational-level, business-level and user-level) are involved in the process as independent, yet accountable members. As the final frontier, external regulators, auditors, and credit rating agencies, need to play proactive roles in identifying risk factors, flagging and following through to closure.

The potential risk of liquidity crisis can be mitigated through these best practices:

  • Creating a robust framework incorporating three ALM pillars viz., ALM information system, ALM organisation (Asset-Liability Management Committee or ALCO) and ALM processes
  • Monitoring structural and short-term dynamic liquidity at both the gap-analysis level and stock approach
  • Maintaining a good balance of high-quality liquid assets and stable funding
  • Leveraging advancements in technology to empower and drive the liquidity governance framework across levels 1, 2, 3 and 4 (external)
  • Early identification, continuous monitoring and remediation of liquidity issues in the short-term and long-term
  • Scheduled stress testing for pitfalls, accounting for both institutional and market risks
  • Planning for contingencies and de-escalation strategies

Across the NBFC sector, asset-liability management (ALM) is at the nascent stage, and needs to be structured at par with scheduled commercial banks. The RBI’s latest guidelines, involving the ALCO, and its rules enforcing new monitoring mechanisms, have fuelled an urgency among NBFCs to adopt technology for liquidity risk management.

In today’s volatile marketplace, the interest rate risk by itself must be closely linked to funds transfer pricing, intraday liquidity and overall capital management. There is a need for a holistic approach using a robust ALM framework to protect earnings and capital while reducing complexity and ensuring compliance.

A standardized system with an independent and targeted governance framework (adhering to RBI regulations and supported by specialist firms) can make an ocean of difference in the financial health of an NBFC. Such a specialist application can help banks and NBFCs meet their immediate liquidity requirements. Beyond this, it can also leapfrog them to the next level of compliance.

NBFCs can use such high-powered systems to:

  • Maintain a proper mix of assets and liabilities to mitigate liquidity risks
  • Build an appropriate mix of rate sensitive assets and liabilities to enable sustainability against Interest rate fluctuations, thereby improving the Net Interest Margin (NIM) and increase of shareholder’s value
  • Maintain high levels of asset quality and liquidity through regular stress testing using Basel III recommended LCR, accounting for potential threats from select business streams
  • Measure concentration risk like Top 20 depositor’s ratio, funding sources, counterparties etc.. that enables FIs in diversification of their funding profile
  • Robustly monitor intraday liquidity position, stress testing and sensitivity analysis for the FIs to prepare for potential risk events
  • Implement and enable a liquidity workflow system across the governance framework, involving various stakeholders
  • Track and record transactional activities for audit and action
  • Identify, measure and remediate shortfalls on real-time and periodical basis, converting data into analytical insights
  • Stay future-ready and flexible to accommodate the changing demands of users, evolving business models in FinTech, and the fluctuating regulatory landscape, using highly-scalable micro-services based architecture

rt360

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. Made in India – and built by bankers for bankers – rt360 is a game-changing solution suite that provides NBFCs the ability to drive sustained growth. The competencies it brings to the picture place NBFCs in a competitive position to take advantage of dynamic market changes while mitigating the associated liquidity risks.

Capabilities:

  • Time buckets for measuring liquidity risk
  • Residual maturity pattern for measuring liquidity gaps
  • Intraday liquidity management through BIS metrics and monitoring tools
  • Liquidity risk management through stock ratios including LCR, NSFR, balance sheet ratios, funding concentration
  • Interest rate risk in the Banking Book (IRRBB) measurement
  • Duration computation
  • Stress testing/scenario analysis on liquidity and interest rate risk
  • Mapping of general ledger heads
  • Business rules-based scenario/what-if-analysis
  • Granular insights with interactive dashboards
  • Behavioural studies for non-maturing items
  • Impact analysis
  • Cash flow accounting
  • Data aggregation and multiple risk reporting formats, leveraging BCBS 239 principles
  • Open architecture for seamless integration with multiple touch points
  • Increased scalability and high degree of customization

Benefits

Adopting a comprehensive, automated approach towards ALM through rt360 offers several measurable benefits:

  • Liquidity risk mitigation thereby enabling NIM and equity in both the short-term and long-term respectively
  • Improved ability to strategically hedge interest rate risk by measuring the impact of interest rate fluctuation on NBFCs’ P&L (NII) and equity (EVE)
  • Early identification of intraday liquidity gaps to meet potential stress situations
  • Improved regulatory compliance by adhering to Basel guidelines, and automated regulatory reporting on liquidity and interest rate risk
  • Accelerated decision-making, due to end-to-end automation of data aggregation, reporting, and superior visualization through dashboards
  • Automated stress testing for short- and long-term, institution-specific and market-wide scenarios, enabling NBFCs to maintain adequate liquidity and capital under stress conditions

Authors

Dr.Jaya Vaidhyanathan

CEO, BCT Digital

Dr. Jaya Vaidhyanathan is an independent Director on several Boards and is focused on bringing in the best global corporate governance principles to India. Her work has found coverage in top news websites like The Hindu and The Times of India. Recently, she pioneered award-winning Early Warning Systems for Indian banks, which have found acclaim in the industry and among counterparts.

Share On

Also Read

Thought Leadership
By rt360 January 13, 2020

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

Read More
Thought Leadership
By rt360 January 13, 2020

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

Read More

Continue reading “Measures to maintain liquidity and asset quality through good governance”

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

The financial world is not without risks. As part of their fiduciary duties, banks operate with intense systemic risks every day, while empowering small firms to large multinationals begin new ventures, innovate and incubate, and ultimately act as the custodians of trust. If not carefully monitored, these systemic risks can easily snowball, and this can impact not only the banking network, but also the financial health of the country at a macroeconomic level.

A real-world scenario

When a corporate takes a loan has taken loan from a bank for building a plant. Normally, the bank will disburse the loan amount in tranches, using which the borrower will continue to pay suppliers for plant construction. Now, let us assume that the borrower attempts to defraud the bank by diverting the loan funds. In the guise of making vendor payment, the borrower sends the money to a “distributor” – a shell corporation that exists only on paper. Can the bank be defrauded?
Let us examine the whole gamut of information to which the bank is already privy:

  • The list of approved parties with whom the borrower is expected to transact for the project. Source: Project documentation
  • The fact that the sum transferred to the “distributor”, is very similar to the amount disbursed by the bank for the project. Source: core banking and transaction systems
  • The purpose of availing each loan installment. Source: the CA certificate to be mandatorily submitted to the bank
  • Whether the distributor is a blacklisted company or in the news for the wrong reasons, subject to frequent tax raids or audits. Source: Big data

If all of the above information (available at the different branches and locations of the same bank) can be shared with the authorities on time, the bank can proactively prevent fraud and save itself from an unsavory and litigious situation involving painful asset quality deterioration.

Credit risk management in perspective of the RBI mandate

The premise for Early Warning Systems is set here It all begins with credit risk. Broadly speaking, there are two aspects to defaults – ‘inability to repay’ and ‘no willingness to repay’. Both could potentially result in NPAs or Non-Performing Assets.

Following the Asset Quality Review of 2015, the RBI rolled out a string of regulations mandating the adoption of EWS as a best practice in identifying and mitigating the risks posed by Red Flagged Accounts (RFAs). The guideline issued mandated systems that would consider 45 indicators of stress in borrower accounts, measure the accounts with respect to each indicator, and flag incidents to authorities. Indeed, a laudable effort from the RBI.

Technology service providers were able to unearth much more data on borrowers from big data sources in addition to traditional data sources and this has aided with insightful decision-making:

  • Massive data ingestion and analysis of loan portfolios of banks across the country, products, and industries/customer segments, to take management calls on pulling back or expanding credit to specific sets of customers
  • Detect the stress of borrowers from what is reported in semi-public sources, including legal cases, share pledging, dubious business dealings, and so on.
  • Listen to rating agencies on what they are saying about their borrowers, industries or the economy
  • Listen to online and social media chatter on the promoters of a borrower

Now is the opportunity to leapfrog from just analyzing the transactional data of borrowers’ accounts to looking at them strategically.

To begin with, the sanity of data itself is a big factor. The key is in knowing where to look for data and when, and this is no easy task. If we examine industry-leading banking risk management systems, like rt360 built by BCT Digital, which are custom-built for the Indian banking sector, they have some of the most extensive sources and credible touchpoints, making data compilation all the more effective. The advanced algorithms and rules engine are extremely effective in mitigating false positives and unwarranted alerts – an area that is particularly hard to manage. The rules as such are far more exhaustive; for instance, rt360 is configured to flag 200 warning scenarios – well more than the 45 proposed by regulators.

How Artificial Intelligence is transforming Early Warning Systems

Early Warning Systems rely on tens of thousands of data points to measure and monitor risks, which is almost impossible for humans to replicate. Artificial Intelligence can transform Early Warning Systems, enabling them to make instantaneous predictions and extract actionable insights from disparate data sources, using these four distinct transformative components:

  • Collating data from multiple touch points
  • Cleansing, validating and restructuring data into valuable information
  • Algorithmic processing using next-generation technologies and data modeling to generate insightful early warning signals/alerts
  • Case management by channeling alerts to decision-making authorities

The future will reveal to us the role of EWS in strengthening asset quality. Furthermore, for the system to achieve its full potential, there needs to be open collaboration between the bank and its technology partner, and this is where partnering with a service provider that has specialized risk management expertise is bound to show results.

Authors

Dr.Jaya Vaidhyanathan

CEO, BCT Digital

Dr. Jaya Vaidhyanathan is an independent Director on several Boards and is focused on bringing in the best global corporate governance principles to India. Her work has found coverage in top news websites like The Hindu and The Times of India. Recently, she pioneered award-winning Early Warning Systems for Indian banks, which have found acclaim in the industry and among counterparts.

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.

Share On

Also Read

Thought Leadership
By rt360 January 13, 2020

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

Read More
Thought Leadership
By rt360 January 13, 2020

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

Read More

Continue reading “Managing credit risk in a volatile financial market with Early Warning Systems”

Innovation Sandbox and Indian Banks – A close look at one of RBI’s most visionary initiatives of our time

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

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

“Innovation sandbox” – the phrase itself is so liberating. A place where thinking out of the box is the norm; the only rule is that there are no rules (or maybe just the bare minimum) and where mistakes are not only pardoned; they are encouraged. But how applicable is it in an institutionalized and conformist set-up, like banking, where rules and regulations are hardwired into the system and straying far from the rulebook can have serious, often fatal, repercussions on the economy of a nation?

In 2019, the RBI formally announced a draft “Enabling Framework for Regulatory Sandbox”, or the innovation sandbox. The framework seeks to enable technology-led companies to build (subject to limitations) and test financial products or services that facilitate innovation and positive change within the Indian banking industry, in return for possible regulatory relaxations, prior to actual launch. In this manner, it will attempt to bridge the gap between innovation, technology and the banking sector.

The RBI framework and the “amazonification” of banks

It is easy to see why the innovation sandbox is extolled by the industry as a welcome initiative by RBI. For one, similar initiatives have seen widespread success in other countries, UK being the first1, and later in Singapore and Estonia, which are shining examples of innovation in the digital realm. However, will the sandbox meet with unequivocal success in the Indian banking context?

The topic is certainly debatable, but if previous instances have taught us anything, it is that change is good, but disruption, even better.

If we take UPIs as an example – there was a time when the technology was still in the nascent stage and adoption rates were low. Before we go into the triggers that launched UPI to the fore, there are two factors we need to consider, in the context of UPIs.

  • 1. Banks by nature are reluctant to share internal and user data, which they consider sacrosanct.
  • The back-end technology is not immune to risks, given that data changes hands several times – between the telecom service provider, the bank and a third-party gateway integrator.

The NPCI, who developed the system2, was quick to identify the need for a framework to manage the risks and bottlenecks associated with UPIs. They released an experimental set-up – a controlled interface for all parties to collaborate on, learn and improve. This was perhaps one of the early triggers to the launch of the innovation sandbox in India. As of today, with close to 800 million transactions in March’193, UPIs are a highly effective industry-specific innovation, and a game-changer for the Indian banking industry.

At a glance: multiple benefits

In effect, the innovation sandbox interfaces a “black hole”, which is constituted by previously inaccessible core banking data, with technology innovations. In the above manner, working within a controlled environment, it will drive across-the-board innovations that can simplify banking related processes – for example, speed-up payments, lower risks, reduce transactional costs and so on.

Another aspect working in favour of the sandbox is the current “amazonification” of the banking industry aimed to connect bankers to new-age customers. The millennial population in particular need banks to become more contextual in their understanding of user needs. So, we have simple algorithms tracking usage patterns and collecting data to dispatch relevant and targeted information (e.g., promotional offers) to users. There has also been an exponential increase in the number of channels by which banks can interact with customers. Beyond the regular kiosks and bank branches, there are the mobile devices, credit/cards, UPIs, doorstep banking, ATMs and so on.

With this explosion in touchpoints and technologies come more vulnerabilities and more risks.

The airline industry was one of the most recent victims; a reputed airline was defrauded of millions in a scam. These sorts of occurrences call for an ecosystem where innovations are not only nurtured, but risks are identified and averted in the nick of time. The controlled yet real-life environment hosted by the innovation sandbox not only places confidence on technology service providers, but also provides them access to customer feedback right from day-1. This in turn reduces iterations, fast-tracks time to market and lowers costs. A calibrated launch model helps to limit risks and control losses for stakeholders, which empowers them to think and act freely, and work cohesively with banks towards mutually beneficial goals.

The flip side: Manifold risks

The absence of a strict policy on customer data privacy is one of the primary hindrances to the guaranteed success of the innovation sandbox. All said and done, the success of the innovation sandbox is directly related to the extent at which private user data, transactional records and confidential information are made available for experimentation. And as with all experiments, things can seriously go wrong. To ensure this is not the case, beyond the present Information Technology Act, India needs a strong policy restriction, as in the case of Europe with its GDPR regulations.

Of lesser magnitude, yet a concern nonetheless, is the fact RBI regulations expressly prohibit testing on cryptocurrencies. Blocking progress in this domain, especially given how Blockchain technologies are gaining traction across the globe, can be very limiting.

Fighting fire with fire

As things stand, it is too early to comment on how and when the concept of the innovation sandbox will finally take flight, and up to what extent. But there is no doubt that the RBI initiative has vast transformative potential. Current risk management systems, which manage and predict credit, liquidity and operational risks, make use of statistical models to make accurate and timely forecasts. The innovation sandbox can open new avenues of assessing, measuring, monitoring, controlling and preventing risks, while improving access to vast repositories of user and banking related data by bypassing regulatory restrictions. Equally noteworthy is how this collaborative ecosystem will accelerate technology adoption, promote out-of-box thinking and increase competitive user offerings.

But perhaps most important is its role in building solutions to issues that have been long plaguing the Indian banking system, including money laundering and NPAs. The current state of the industry, which is in dire need of innovative fintech intervention, dwarfs any apprehensions of data privacy violation, provided the RBI heightens measures to protect the use of valuable and confidential information.

Authors

Dr.Jaya Vaidhyanathan

CEO, BCT Digital

Dr. Jaya Vaidhyanathan is an independent Director on several Boards and is focused on bringing in the best global corporate governance principles to India. Her work has found coverage in top news websites like The Hindu and The Times of India. Recently, she pioneered award-winning Early Warning Systems for Indian banks, which have found acclaim in the industry and among counterparts.

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.

Share On

Also Read

Thought Leadership
By rt360 January 13, 2020

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

Read More
Thought Leadership
By rt360 January 13, 2020

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

Read More

Continue reading “Innovation Sandbox and Indian Banks – A close look at one of RBI’s most visionary initiatives of our time”

Practitioners’ Insights On Credit Monitoring

In an industry-first survey on Credit Monitoring Practices of Indian Banks, Bahwan CyberTek highlights the need to take a holistic approach to credit monitoring with organizational ownership combined with an urgent need for automation as well as data integration.

Bahwan CyberTek, a leading global provider of innovative software products and solutions, has launched a report based on India’s first ever survey on ‘Credit Monitoring Practices of Indian Banks’; the report titled ‘Practitioners’ Insights on Credit Monitoring’.

In an industry-first survey on Credit Monitoring Practices of Indian Banks, Bahwan CyberTek highlights the need to take a holistic approach to credit monitoring with organizational ownership combined with an urgent need for automation as well as data integration.

Bahwan CyberTek, a leading global provider of innovative software products and solutions, has launched a report based on India’s first ever survey on ‘Credit Monitoring Practices of Indian Banks’; the report titled ‘Practitioners’ Insights on Credit Monitoring’. This was part of an event organized by the company where industry leaders and practitioners from private and public sector banks shared their experiences, both from a regulatory and a banker’s viewpoint and the latest regulatory developments in the Indian banking system, for credit monitoring.

The survey was conducted between October 2016 and February 2017, amongst senior bankers spearheading the credit risk monitoring or are part of the management unit, spread across 25+ public and private sector banks in India of varying asset sizes; this included banks whose total asset size comprised 42% of the combined asset size of all Indian scheduled commercial banks, as of March 2016.

Commenting on the launch of the report, Jaya Vaidhyanathan, President – BFSI & Strategic Business Initiatives, Bahwan CyberTek said, “We at Bahwan CyberTek believe in proactive action as opposed to merely reacting to an action. As a key priority for 2017 and years to come, we think it is important for Indian banks to make full use of the technology which will help automate and therefore improve their credit monitoring techniques, given that the health of our country’s economy depends on it.
“This therefore brings about the need for an Early Warning System where bankers can predict and assess the health of a borrower, for instance, and take the necessary measures. Moreover, such a system should help gauge the performance of all critical sectors that contribute towards the growth of the Indian economy”, she added.

Some of the key findings from the survey include:

  • No bank has completely automated the SMA (Special Mention Accounts) monitoring process
  • Banks largely rely on internal data to monitor the borrower health that might cause trouble in the future; the absence/ minimal use of external data (from an availability and quality point of view) doesn’t help in taking sound decisions.
  • Majority of the responding banks (>70%) see a need for a separate Early Warning System rather than making modifications to the existing systems, and have planned for one.
  • With regard to reporting, data collection is fully manual for 40% of the responding bank
    • For over 75% of the banks, report generation, dissemination and follow up actions is either fully manual or just partially automated

Speaking at the launch, T. N. Manoharan, Chairman, Canara Bank, said, “I would like to congratulate Bahwan CyberTek for undertaking this survey. I am sure that the findings will be of immense value to both banking personnel and the BFSI industry as a whole, and I personally hope to be enriched by the insights presented in the report. With regard to the banking industry, the two major challenges faced by the sector in the last one year have been the Asset Quality Review issued by the RBI, and demonetization. However, the banking industry is returning to normalcy, after having faced these hurdles.”

Share On

Also Read

Thought Leadership
By rt360 January 13, 2020

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

Read More
Thought Leadership
By rt360 January 13, 2020

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

Read More