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

Key trends in Model Risk Management in 2021 - rt360

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

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

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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