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Application of Data Science in Managing Risk of Banks & Financial Institutions

More and more businesses today are riding the wave of analytics and the same is the case with the banking and finance sector. With the rise in computing power coupled with advanced analytics techniques, they can extract deeper and more valuable insights from ever-growing astronomical amounts of data. 

The BFSI sector has come under intense regulatory scrutiny following high profile financial frauds related to money laundering, tax evasion, terrorist financing, etc.  Risk management then becomes one of the most important areas for financial institutions, responsible for identifying, evaluating, & prioritizing risks followed by approaches to mitigate or minimize this impact.  Over the past few years now, financial institutions have been leveraging the power of big data and machine learning models for risk assessment and robust decision making.

Below are a few of the areas where banks and financial institutions are assessing and managing risks with the help of Data Science & advanced Machine Learning techniques.

 

General Risk Assessment

Financial institutions leverage data from different internal and external sources such as transactional data, behavioral data, credit-bureau reports, market data, geospatial data and also employee & customer data from emails, chats, social media, etc. and Data Science techniques to explore patterns, analyze various perspectives to detect potential risks and more effectively in mitigating those risks.

 

Credit Risk Assessment

Advanced-Data Science techniques can be used to build a robust predictive model to assess loans and customers. Internal & external data can be used to build a model to get deeper insights that can be used to understand customer behaviors and intentions. Data science models can be trained on thousands of customer profiles with hundreds of data points for each customer for better results with higher accuracy as to which customer is a safe bet for lending and which could be a potential defaulter.

 

Anti-Money Laundering

Money laundering is one of the major concerns for financial institutions. In order to fight against money laundering activities, financial institutions are leveraging the power of big data and machine learning models. With the help of Analytics & Natural Language Processing techniques on structured and unstructured data, hidden patterns and suspicious activities can be identified between accounts and transactions. Advanced data science techniques can be used to analyze a real-time stream of data and money-laundering activities can be spotted and stopped from taking place at the earliest stages.

 

Financial Market Risk Assessment

In modern financial markets, portfolio management involves various risk analysis methods and simulations. One such simulation, Monte Carlo, allows you to see all possible outcomes of your decision and assess the impact of risk accordingly. Advanced analytics can help in improving the quality and speed of these simulations to better calculate Credit Valuation Adjustment (CVA) at the portfolio level, and make more sound investment decisions.

 

Operational Risk Assessment

 Operational risk is one in which a firm fails or loses money due to human error or failed internal processes and systems. These risks include Fraud, Natural Disaster, Internal policies, hacking, etc. 

Big data and advanced data science techniques can be used to create highly efficient systems to detect and prevent fraudulent or speculative trading and regulatory breaches. By analyzing structured and unstructured data, suspicious patterns can be identified & fraudulent activities can be tracked and prevented from taking place.

February 4, 2020

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