How to Solve 3 Common Data Challenges
Financial companies are often leaders in data-driven practices. We explore how to avoid or overcome three common data challenges these providers face.
- By Stan Pachura, Shikha Verma
- August 15, 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
Below we suggest tactics for turning those challenges into opportunities to drive growth, attain compliance, and reduce risk.
Data Transparency for Regulatory Compliance
The financial crisis that started in 2007 resulted in mandated (CCAR) stress testing to ensure financial institutions were rigorous and transparent in their capital planning. The goal was to reinstitute the shattered customer confidence in the banking industry. Failure in these stress tests caused severe damage to a firm's reputation as well as possible penalties. Data transparency is critical for success with CCAR and other regulatory compliance tests.
Data transparency involves knowing how and where the organizational data for capital planning is gathered, aggregated, and used. This is a must for regulatory compliance and auditing at financial institutions.
In our experience, most large companies struggle with transparency of data and data-related processes. The most common causes are lack of consistency in processes across business lines and lack of intuitive and simple tools that enable transparency.
Use these three steps to address data transparency challenges:
- Streamline capital planning process across investment, retail, and consumer banking
- Implement governance processes across capital planning and other data so users can drill down from high-level summaries into detailed data
- Choose the right tools that enable data transparency across the enterprise
Data Insight to Reduce Aggregated Risk and Fraud
Aggregated risk analysis, money laundering, fraud, rogue trading, and data breaches are top of mind at financial institutions. Although the new interconnected world causes some of these challenges, data can also be used to detect some criminal behaviors at the onset and reduce organizational risk.
Aggregated risk management is an enterprise's ability to identify total risk exposure, analyze it, and act on it. The aggregation of risk and the opportunity for fraud go far beyond individual consumers, explicit demographics, or specific geographies.
Think about these three tips to reduce risk and fraud:
- Aggregated risk analysis became a must after the financial crisis in 2007. Data enables enhanced risk analysis and reporting. Use data to look for risk patterns and turn this "check box" activity into a competitive advantage.
- List patterns of potential fraud and determine which can be handled automatically by algorithms. This process has been extremely useful for some companies we have worked with. Fraud algorithms and machine learning can greatly reduce risk by identifying dangerous behavior patterns before fraud occurs.
- External data about geographic conditions, industry concentrations, employment diversification, and agricultural dependencies can be integrated with proprietary data to further enhance analyses and provide a more robust risk profile.
Data-Driven Customer Intelligence
Customer intelligence is the understanding of motivations, patterns, and trends in consumer behavior that provides insight into how a company can increase sales, improve products and services, and enhance customer satisfaction. This insight (and how to use it in the marketplace) has been the "brass ring" for financial service companies for decades.
Cross-selling is a prime example, but the benefits of customer intelligence go far beyond this. To maximize customer satisfaction and enhance the experience for today's consumer, a financial services organization must understand the desires and behaviors of its clients. Customer data can be integrated across platforms (especially helpful when the financial institution has merged with other firms), across channels, and across lines of business to present a single view of the customer and allow the organization to think differently about the relationship.
Consumers are more connected than ever before. As information about a consumer's use of mobile access points, the Internet of Things (IoT), financial services apps, social media, and other customer touch points grows, the use of customer intelligence as a differentiator will continue to increase in the industry.
By analyzing data in real time, financial services companies can immediately offer new products, alternative pricing, or balance-increase options, all while the consumer is still active at the touch point.
These three ideas can help you make timely, proactive decisions and exceed both corporate and customer goals:
- Customer intelligence can only be obtained by combining and analyzing data from a variety of sources. To effectively gather and act on this consolidated data, organizations must first have the enabling technologies and architecture in place.
- Data in an organization might be siloed, inhibiting or preventing an enterprise from assembling a comprehensive view of the customer in real time. The data platform may not be able to perform at a level adequate to deliver comprehensive information in a timely and usable manner.
- Businesses must identify the systems, policies, and procedures that cannot support their innovation and strategic positions and pursue alternatives.
About the Authors
Stan Pachura is president of Strategic Path Consulting, which provides services to CIOs and technology and business operations that enhance operations and optimize use of information and technology for outstanding business results. Stan previously served as chief information officer for two publicly traded financial services organizations where he provided creative direction and supported their innovative and strategic visions. Stan can be contacted at [email protected].
Shikha Verma is senior vice president at Diyotta, a data integration company. She is a data and analytics strategist, leader, and advisor to several companies in Silicon Valley. In her career, Verma has helped several Fortune 500 companies realize multi-million-dollar value from monetizing their data and deploying the right analytics. You can contact the author at [email protected] or at https://www.linkedin.com/in/shikhaverma.