Data Solutions that Improve Risk Management for Banks
Any successful banking and finance institution should maintain a surveillance program around exposure to risk and associated losses to protect the value of their assets.
The complexity as well as rapid volume and pace of financial data demand that these institutions continuously evaluate risk by reviewing their processes, policies, and controls. Organizations at the forefront of risk mitigation and management are beginning to discover that an easy way to reduce exposure is to transition from traditional risk management tactics, which are largely manually intensive toward modern, automated data solutions. This can certainly streamline processes and, more importantly, provide real time monitoring capabilities so that when a threat is identified, it can be dealt with post-haste.
Based on industry and technology trends, it’s becoming evidently clear that risk management systems will depend more on the insights provided by big data as well as applications that utilize artificial intelligence (AI) and Data Science. In recent initiatives, banks have already begun seeing dividends of big data analysis to better manage risk, assure security with encryption, and meet compliance requests. Our experience has taught us that big data can be used to form immediate, forward-looking, and fact-based key risk and performance indicators (KRI & KPI) for individual banks and their products as well as various financial management services.
Dynamic Key Risk Indicators Enhance Risk Management for Banks
Something we have noticed and certainly worth keeping an eye on is the shift from static KRIs, what most banks are utilizing today for their risk identification, to dynamic key risk indicators. This critical measure utilizes big data as it moves from past to present to future. Here is a use case showcasing the benefits of dynamic KRIs utilizing big data:
A $2 billion-asset bank is keeping tabs on all 380 tenants in properties where it is the CRE mortgage lender. Due to the adoption of dynamic KRIs they can extract big data from external sources and couple it with their internal data and get real time risk analysis of their tenants. So, if an accounting firm in one of their office buildings are named in a lawsuit, the bank’s risk management systems notify them. If a dental practice has its credit score downgraded, they will know about that too. 1
Now, the bank has the capability to be proactive with their decision-making. This gives them the option to deal with at-risk clients either by adjusting the life of the loan, increasing loan guarantees and/or lowering the loan-to-value ratio.
AI and Data Science Help Banks with Risk Management
Another data solution banks and financial institutes can harness to improve risk management capabilities has to do with AI and Data Science applications. These powerful resources can assist with:
- Credit risk modeling
- Increase early risk identification
- Analyze of incoming & outgoing cash flows for liquidity management
- Deploying cognitive computing products to improve surveillance & prevent employee misconduct
- Automated filing of regulatory compliance (as well as receive notification of regulation changes)
Additionally, AI and Data Science resources can prevent money laundering and cybercrimes through improved Know Your Customer (KYC) programs. For those not familiar, KYC is the mandatory process of identifying and verifying a customer’s identity. As an example, AI applications can be setup to capture document templates and run tests to make sure that the document has not been tampered with in any way. It can extract data out of specified fields and verify them. Similarly, biometric authentication, especially face recognition validation, can also be performed, and uses AI to match unique facial features. With effective verification methods in place, KYC analysis can fulfill the need for ‘Know Your Customer’ compliance. Having this framework is important because as banking and finance services move toward more online and digital platforms, the onus of authenticating someone’s identity becomes more challenging, therefore, increasing risk exposure.
In conclusion, modern data solutions to improve risk management, such as the ones discussed in this article, are quickly becoming the expectation of future ready banks and financial services organizations. Due to the volume and velocity of finance data, relying on traditional workflows and manual processes significantly open these institutions to increased risk that can not only be detrimental to themselves via regulatory penalties or depreciation of assets, but also to their customer base that have suitable alternatives at their disposal for their financial needs.
