Risk prevention - a framework for financial institutions
In today's digitally-connected world, applying for a loan is a cakewalk. As a result, the volume of credit applications have gone up exponentially in past few years across virtually every type of loan - from home and car to personal. Processing large volumes of applications and appraising the credibility of the applicants within stringent timelines is resulting in higher risks of default. Consequently, what the banking industry critically needs today is an advanced framework to analyze, detect and prevent risk early in the underwriting process.
Managing uncertainty is an area of significant importance for any financial institution. To effectively manage uncertainty, an institution needs to analyze critical credit history and other data (past and present) of each applicant. To aid this process, almost all credit bureaus worldwide are actively working on incorporating additional measures to augment the raw information provided in the report. Among the most valuable of these measures are early warning alerts.
In this approach, risk is rated and prevented but not managed as such. There is a fine line in between early warning and risk management. These early warnings help to manage risk efficiently and manage portfolio at a higher level. While early warnings can help prevent risk in the short term, risk management is the most effective mitigation approach for the long term. Early warning alerts are created based on several critical factors like past due, bankruptcy record, account balance, trade count, and many more. These alerts are dynamic in nature and keep changing with time.
While credit bureaus make every possible effort to provide financial institutions with early warning alerts that are timely, relevant and accurate, there are several challenges for banks. Some of the major challenges include:
- How do I make proper use of these alerts and utilize them efficiently in risk mitigation?
- How do I incorporate these alerts in the existing system in a timely manner?
- How do I automate my process flow to trigger the right action from each alert?
- How do I prioritize and handle large volumes of early warning alerts?
- How do I generate reports for monitoring and analysis?
Use case: As a credit technology partner of multiple US and European large banks, Infosys has been involved in providing early warning risk prevention solutions - from idea generation to implementation of frameworks. Some of these frameworks addressed the need for banks to analyze and utilize large volumes of risk alerts to effectively mitigate risks.
Primarily used by the underwriting and risk monitoring areas in credit, a risk management framework typically involves:
- Customization of standard incoming alerts and aligning them with strategic objectives
- Analyzing and prioritizing alerts based on critical rule sets to manage large volume of alerts
- Developing in-house analytics tools and techniques for faster decision making
- Establishing a flexible framework to incorporate changes in risk management with minimum effort
- Building a repository over a period of time for internal customer history and fraud prevention
The predictive accuracy of identifying and prioritizing risk is very complex, and experienced analysts can yield value to the organization. It's critical to prioritize early warning alerts to eliminate the threat of losing potential customers. This entails timely identification of alerts and utilizing them to take the right decision. Automating the early warning process still deserves a closer look from the risk experts. Therefore, even if an early warning signal proves true, they can be routed through the proper line of business. This manual effort can be minimized with caution depending on the reliability of the early warning framework. Finally, the process of early warning detection ends with reporting, which refers to periodic analyses of historical and current data.