How to solve 3 common data challenges in Financial Services
Posted By Diyotta Inc.
26 September 2016
Financial services companies have historically been among the first ones to think about data architecture and data-driven customer enablement. These businesses include the traditional banks, insurance companies, and credit card companies, as well as new age fintech companies that provide payment processing or online financial services.
After the 2007-2009 financial crisis in the banking industry, new regulations were introduced to ensure sound capital planning and aggregated risk management in banks, including Basel Committee guidelines on risk data reporting and aggregation (RDA), the Dodd-Frank Act, and regulatory capital adequacy legislation such as Comprehensive Capital Analysis and Review (CCAR).
The advent of these regulations resulted in a greater need for robust data. Later, as the fintech space bloomed and strove for greater integration of consumer and financial data to drive exceptional customer experiences, quality data became even more critical.
With the need to become more data-driven came new challenges and opportunities. In this article, we explore three data challenges:
- The must have: Data transparency for regulatory compliance
- The must do: Reduce aggregated risk and fraud with data insight
- The growth enabler: Data-driven customer intelligence
In this article, we suggest tactics for turning those challenges into opportunities to drive growth, attain compliance, and reduce risk.
We hope that these steps provide you some good food for thought as you plan on turning your data challenges into opportunities. Stay tuned for follow-up articles on each of these three topics as we deep dive with more detailed content and examples.