7 Big Data Analytics Use Cases for Financial Institutions

Big Data Analytics
Big Data Analytics

Recently we hear a lot about Big Data Analytics’ ability to deliver usable insight – but what does this mean exactly for the financial service industry?

While much of the Big Data activity in the market up to now has been experimenting about Big Data technologies and proof-of-concept projects, I like to show in this post seven issues banks and insurances can address with Big Data Analytics:

1. Dynamic 360º View of the Customer:
Extend your existing customer views by incorporating dynamic internal and external information sources. Gain a full understanding of customers – what makes them tick, why they buy, how they prefer to shop, why they switch, what they’ll buy next, and what factors lead them to recommend a company to others.

2. Enhanced Commercial Scorecard Design and Implementation:
Financial institutions use Big Data solutions to analyze commercial loan origination, developing scorecards and scoring, and ultimately improving accuracy as well as optimizing price and risk management.

3. Risk Concentration Identification and Management:
Identify risk concentration hotspots by decomposing risk into customized insights. Clearly see factor contribution to risks and gain allocation consensus through downside risk budgeting.

4. Next Best Action Recommendations:
Make “next best action” an integral part of your marketing strategy and proactive customer care. With analytical insight from Big Data, you can answer such questions as: What approach will get the most out of the customer relationship? Is selling more important than retention?

5. Fraud Detection Optimization:
Preventing fraud is a major priority for all financial services organizations. But to deal with the escalating volumes of financial
transaction data, statisticians need better ways to mine data for insight. Optimization for your current fraud detection techniques help to leverage your existing fraud detection assets.

6. Data and Insights Monetization:
Use your customer transaction data to improve targeting of cross-sell offers. Partners are increasingly promoting merchant based reward programs which leverage a bank’s or credit card issuer’s data and provide discounts to customers at the same time.

7. Regulatory and Data Retention Requirements:
The need for more robust regulatory and data retention management is a legal requirement for financial services organizations across the globe to comply with the myriad of local, federal, and international laws (such as Basel III) that mandate the retention of certain types of data.