Staying Ahead of the Curve: Improving Risk Analysis For Financial Institutions
Today’s financial enterprises face increasing pressure to reduce costs, improve capital deployment while meeting fast-evolving regulatory challenges. For them the need to transform their strategies & processes is vital. At the heart of this change lies the enormous task of managing data.
In my opinion, next-generation data and analytics tools can help banks and financial institutions to improve their data quality and capitalize on the information assets in an efficient manner. Though the need of the hour is twofold. Firstly, firms must use existing data sources that have been lying unused in their business units to produce meaningful information. Secondly, they should manage the new type of data that keeps coming in from different external sources.
How can financial firms address their industry challenges? While there is no blueprint to suggest what a bank’s risk function would look like in 2055, there are some trends that surface and suggest how banks can deliver short-term results while preparing for long term goals.
Here is an overview of the top 5 risk-analytics trends that are gaining traction in the financial spectrum:
1. Credit Risk Analytics: The use of advanced credit risk analytics helps BFSI firms to grow their revenues, and improve underwriting decisions — all at the same time while reducing risk costs. Also, ML algorithms are used to determine patterns and draw meaningful recommendations from large datasets.
Deploying next-gen analytics tools help enterprises understand and adapt to changing consumer behavior. It also helps them to mine vast amounts of existing data and expand the credit ‘buy box’. If that wasn’t enough already, did you know that enterprises can also increase penetration of their customer base and understand the aggregate risk levels using credit risk modelling techniques?
Trust me, the possibilities are endless.
2. Stress Testing: A well-defined process utilizes the ML, Big Data and AI capabilities to generate practice scenarios. Then, it translates them into appropriate environmental parameters using macroeconomic quantification.
Once that is done, the focus of this analysis to assess the impact of the scenarios based on the client’s profit and loss situation and the overall market condition. If you’re wondering why banks and financial services use optimization engines as a part of their stress testing process — because it becomes easy to respond to the regulatory constraints.
According to me, the financial institutions require a well-charted action plan. How does that help? It lets them optimize balance sheets, reduce risks and increase opportunities.
3. Operational Risk & Fraud Analytics: Data visualization and AI-powered data analytics tools can enable financial firms to derive valuable insights. And why do these insights matter? It is because they facilitate enhanced decision making and accelerated growth.
In my experience, advanced models help classify the data into segments by using techniques like data mining and pattern recognition. Then, they help find patterns and automatic associations that can be used to detect fraud. There are also expert systems and neural nets at play that help directly in detecting fraud. But how can ML tactics help financial institutions make better predictions? It is by helping them find patterns that were not identified previously.
Other AI and ML techniques including link analysis, decision theory, Bayesian networks and sequence matching are also very useful in not only detecting fraud but also mitigating operational risks.
4. Insurance Analytics: The insurance industry has been handling large amounts of data for years now. However, one look at how they have been operating and underwriting — and you would realize their slow pace.
AI, ML and predictive analytics can process data faster. But how can that help insurance providers? The answer is simple: It can enable them to make faster data-driven decisions. And then, it further helps them to generate long-term value.
Are there other uses of these techniques? Well, they can help identify market risks, popular trends and the risk potential of every individual customer. In my opinion, the organizations can automate their demand analysis processes to generate new product ideas. It could help them improve the pricing and service personalization methods and bring in more profits.
5. Institutional Investment analytics: Institutional investors can use big data, AI and ML techniques to understand the risk and return potential of portfolios. They can also get a better idea of their risk-adjusted returns. How can that be done? Well — by analyzing the present risk factors and ‘model asset and liability matching’ hacks.
Using next-gen technologies can enable them to enhance their asset allocation methodologies. Firms should embed advanced technologies to build lasting capabilities for the future.
To conclude, at the core of using advanced analytics and setting the stage for success lies the industry imperative to maintain data quality. To improve business decisions, financial enterprises can digitize their underwriting processes and automate major operations.
The transformation in the financial sector can be complete when it is matched by an equal shift in the organizational culture of banks and financial enterprises. By establishing analytics as a true business principle, they can unlock the potential of the future and keep up with the evolving industry challenges. Do you think it would be a good start? Let me know your thoughts in comments.
About Author:
Saumil Shah, the CIO at Rishabh Software, is a technocrat with a successful track record of helping clients solve complex business problems. Under his strategic presidency, Rishabh has established a prominent market presence across multiple domains like Healthcare, Retail, Manufacturing, FinTech & more. He is an alumnus of IIT-Kanpur & London Business School and has been on the advisory panel of industry-leading bodies like NASCOM and GESIA. When he’s not busy solving business technology challenges, he enjoys playing tennis or football.
