If there is any segment in financial institutions or banks that has turned to big data and analytics increasingly, then it is none other than “Risk Department”. Big data and analytics applications are being deployed to ascertain credit risk, market risk and any other non-financial risk. With an increase in the number of financial crimes and frauds, it is crucial to be proactive rather than embrace a reactive approach to unforeseen events.
New financial regulations and reporting standards across the globe encourage the use of big data and analytics applications. After the financial crisis of 2007-08, firms had to deal with tighter regulations and reporting standards that require real time financial reporting and adherence to certain financial ratios, including BASEL III, Fundamental Review of the Trading Book (FRTB), European Market Infrastructure Regulations (EMIR), Markets in Financial Instruments Directive (MiFID), International Financial Reporting Standards (IFRS) etc.,
New regulations address risks which arise from fraud, money laundering, terrorism financing, as well as from high frequency algorithm-trading etc. In order to adhere to these regulations, the institutions need to implement a system which can address issues in a timely manner. Big data and analytics can provide solutions for all these challenges. With the help of big data, financial firms can calculate relevant financial ratios in real time.
Better technology leads to higher customer expectations, compelling banks to come up with customized financial products, failing which they cannot match the customer service levels of new FinTech companies. This creates further challenge for risk management in these financial firms. Big data and analytics can capture relevant customer information and make decisions on credit risk, detect financial fraud or crime, and deal with other issues related to portfolio of customers. This facilitates risk monitoring.
Risk function department in financial institutions uses vast amount of data and data models. Value at Risk (VaR), Expected Shortfall (ES), Stressed VaR, and VaR Backtesting are some of the methods adopted to measure risk. In addition to these, other simulation methods like Monte Carlo Simulation, Historical Simulation; scenario analysis and sensitivity analysis, all use huge amount of data and different types of data modeling techniques. Financial institutions turning to Big data, analytics and machine learning is thus inevitable.
Machine learning helps design risk models and find new patterns which boost predictive power. With the help of new models, financial institutions can reduce the manual cost and increase efficiency and effectiveness of the risk function to finally gain a competitive edge.
At one end, technology assists risk functions but also poses new threats such as reputation risk, model risk, cyber security risk, etc. risk, etc. Though all these risks increase due to technology and use of different data models across the globe, they can be resolved with the use of technology. Regular analysis of each type of transaction, sophisticated machine learning algorithm, and analytical set up help detect the risks and block transactions when irregularities or high risk activities are observed.
Due to strict regulations and reporting standards, most financial institutions and banks witness decline in margins. Due to other FinTech companies and advent of digital players, margins will be affected further. This requires risk function to become technology savvy by reducing human intervention. The objective can be achieved by implementing more sophisticated analytical system, using cognitive technology like artificial intelligence, machine learning etc., which will bring about standardization. Risk department will help the banks to take optimal risk by ascertaining potential threats, which boosts their performance. So, risk department can deliver more without any additional cost by adopting these technologies and bring about standardization to the risk function.
Most of the Indian IT firms which assist financial institutions and banks can leverage the new technologies, big data, analytics, artificial intelligence, machine learning and other cognitive technology to further optimize risk function of these banks. Though most of the IT firms are associated with data cleansing and data validation, there is limited scope for them to use these functionalities. However, IT firms should proactively work towards delivering better service by optimizing the current process with these new technologies. This will improve customer satisfaction levels and help establish better relationship with clients.
Due to the introduction of new regulations every year, there is ample opportunity for IT service providers to develop new cost effective technological platform which help banks achieve regulatory compliance. As Basel IV and MiFID II are going to be implemented next year, companies should use this opportunity to provide enhanced service and cater to the bank’s requirements.
IT firms can work with these banks to develop new risk models for risk measurement. Since IT firms have technological knowledge and financial firms have domain knowledge, collaboratively they can build robust and sophisticated system using cognitive technology which in turn will reduce the risk function cost for banks.
As complete risk prevention is not possible, financial institutions need to be more vigilant towards risk events and reduce their impact. IT firms can provide the solutions by designing risk models which detect risk event when they take place and mitigate their impact.
Based on the claims made above, we can conclude that in a networked economy, collective risk management using big data and analytics will reduce financial risk significantly.