5 Use Cases of Predictive Analytics in BFSI

Predictive analytics help the banking and financial sector to transform customer experiences. Using data, the industry can more easily predict future patterns and trends. This data helps to reduce business costs and associated risks while delivering more personalized experiences to customers. The everyday uses cases detailed below offer further convey the importance of predictive analytics and how the use of historical data can impact future decisions. 

1. Fraud Detection

One of the largest concerns for the banking and financial industry is fraud. As technology continues to evolve, the risk for cybercrimes grows. With institutions feeling increased pressures from customers and threats from cybercriminals, they continually undertake measures to try and prevent fraud. More and more, institutions use big data tools and advanced algorithms in their fight to prevent fraud cases from rising. By studying customer behaviors and existing fraud patterns, institutions and insurance companies alike utilize predictive analytics as fraud detection prevention. 

2. Engagement/Churn Prevention

Banks and financial institutions use predictive analysis to gain a competitive edge in the market. For this industry, acquiring new customers and customer retention are key for maintenance and growth. Predictive analysis allows institutions and insurance companies to gain important insights on potential customer segments, helping to enhance customer acquisition. Regarding retention efforts, analysis allows for the identification of customers willing to switch to other providers and the reasons for their behaviors, therefore helping institutions to curtail customer abandonment.  

Customers are looking for personalized experiences when it comes to managing their finances. A recent report, Chart Your Course To Successful Personalization In Banking, from Forrester details the importance of personalization and highlights ways banks can find success:  

• Align strategy and technology 
• Map and prioritize customer outcomes to help drive business objectives 
• Collaborate to coordinate technology investments 

With loyalty being a big concern for banks, analytics also provides vital information on current customer habits, including spending and behavior, which allows them to shape customer retention and implement loyalty programs. Analyzing customer satisfaction is important regarding churn prevention as the overall costs to acquire new customers are greater than costs to retain existing customers.

3. Customer Targeting and Lifetime Value

Understanding customer behavior and trends is a way for banks to target and improve the lifetime value (LTV) of their customers. Predictive analytics helps banks to determine what loyalty programs work for current customers. Mapping out what works enables banks to implement strategic engagement efforts to acquire new customers. Additionally, predictive analytics offer insights into customer buying patterns, enabling banks to recognize which products they should introduce. 

Improved customer service is a byproduct of effective customer targeting. Through customer analysis, banks can effectively run targeted campaigns, further engaging with customers. Customer responses help to boost interaction, enabling banks to improve the LTV of a customer. Being able to cross-sell not only leads to greater profitability for banks, but also allows them to be proactive and meet customer expectations. 

4. Liquidity Strategy

Witnessing so many disruptions during the pandemic, it’s evident that all industries must plan accordingly for any events having the potential to impact business or the lives of customers. Banks have standards in place, one of which, the liquidity coverage ratio (LCR), requires banks to have enough high-quality liquid assets, ensuring their ability to meet obligations during times of financial stress, like the pandemic. With that said, and to dial it back a bit, let’s consider plans businesses need to have in place to service the daily needs of their customers. Predictive analytics is essential to daily planning for banks and other financial institutions. By understanding traffic patterns and customer habits, banks can strategically implement cash liquidity plans guaranteeing enough cash on hand in physical locations and ATMs to properly service the needs of their customers. 

5. Managing Collections and Risk Management

Two aspects of banking that are vital to their inherent existence are lending and collecting. Banks need to keep meticulous records when it comes to collection. A major component of a financial institution’s overall strategy is risk management. It is so essential because it incorporates elements of trust, decision-making, and security. As opposed to past measures, today, banks utilize data and AI to deal with and mitigate risks. Banks look to scoring models to help make decisions and to uncover predictive information relating to delinquency, default, and pricing. 

Algorithms provide banks with an upper hand, allowing them to have better insights into consumer spending and to gauge the creditworthiness that potential customers exhibit. Why is this so important? Based on this information, banks can make informed decisions relating to individual credit limits and lending, helping to curb potential defaults. Touched on earlier, predictive analytics also helps banks with customer retention efforts. In this sense, utilizing models helps banks to forecast if a customer will abandon their institution. 

Redefining Customer Management

Predictive analytics help banks and financial institutions to predict consumer behaviors and preferences. Understanding customer patterns allows businesses to gain a competitive advantage in forecasting, planning, and making decisions aligning with the best interests of their clients. They are greater equipped to uncover trends and patterns, enabling them to meet the current and future expectations of their target audience more easily.  

Using historical data, business leaders can make informed decisions; better shaping their future and increasing their potential for higher profit margins. Data collection enables banks to react to changing market conditions, identify risks, make forecasts based on past consumer behaviors, and identify new opportunities for both them and their customers. Predictive analytics will continue to play a key role in the financial services industry for many years to come. 

Apexon enables enterprises to make more informed business decisions in relation to future patterns and trends. To learn more about predictive analytics, check out Apexon’s Data & Analytics services  or get in touch with us directly using the form below.