Financial services data solutions

Knowing your customer requires insight into the customer lifecycle, quickly connecting customer data across your organization, and understanding the context in which they are engaging.

In most cases, financial services data isn’t lacking in quantity – it’s the quality and completeness of the data that can cause very damaging, expensive headaches.

While customer information files (CIF) have previously been relied on to connect data across an organization, end users still need to put in manual effort to keep up with customer data that’s constantly changing – this type of data management often results in incorrect, incomplete information. As they age, CIFs can become less effective, and simply aren’t designed in a way that effectively integrates them with existing core bank processes.

This leaves you with an incomplete view of your customer, the consequences of which trickle into your marketing, operations, and data analytics across all channels – as well as regulation, risk management, and underwriting.

The solution boils down to a single, comprehensive 360° view. Harnessing a powerful combination of robust data quality addressing capabilities and graph-database technologies means that the days of error-prone multi-tool data management are gone – and bigger, better decisions are ahead.

Visibility and context create new levels of trust and insight into your customer data, empowering the creation of streamlined, personalized experiences and offers that customers have come to expect. For your business, it means bigger and more rapid ROI and the ability to stay ahead of the competition.

Happy customers, booming business opportunities: what could be better?

Financial services organizations have long been targets of financial crimes, with anti-money laundering (AML), identity fraud, and privacy/security issues top of mind.

Compliance penalties and fines have plagued the industry, thanks in no small part to complex regulations that are heavily regulated and ever-changing – like Know Your Customer (KYC), AML, GDPR, and CCPA, to name a few. Staying on top of these regulations is no easy task, and screening against more lists and generating more alerts only leads to an increase in false positives – up to 98%, in fact.

The cost of compliance issues hit more than just your organization’s bottom line – they take their toll on brand reputation and customer loyalty. Negative press regarding these issues travels fast, and with your competition just a click away, the consequences of inaction are simply too high to risk.
When you consider that most screening platforms don’t include data quality or entity resolution capabilities, and the costs of investigator data gathering and alert review are high, what’s the best solution? Reconsider the screening process end-to-end and improve the quality of your data before the painstaking process starts.

The right financial data solution provides fully auditable matching that ensures AML and fraud compliance with effective data quality and entity resolution, so you can efficiently and effectively detect and escalate alerts for suspicious activity. That means avoiding false negatives, reducing false positives, and resolving alerts faster – and saving your investigation team valuable time.

A strong financial services data governance framework plays a pivotal role in establishing processes that identify and enforce data policies related to regulatory requirements and FCC. It ensures that you can easily search, understand, and trust critical data assets across systems, to enable more accurate, informed decision-making and reporting – resulting in more efficient risk evaluation that protects your organization’s wallet and reputation.

It’s no secret that the need for brick-and-mortar businesses has been dwindling, given the rise in digital channels and consumers’ demand for convenience.

Financial services organizations aren’t exempt from this trend, and when it comes to branch location optimizations, decisions need to be made strategically to maximize the return on those hefty capital expenditures.

What does the market look like? What’s the optimal number of branch locations given the demand? If considering renovation, which attributes are likely to have the greatest impact? The questions to consider during the process are seemingly endless, but their answers all lie in data-driven location analytics that give you the ability to visualize trusted data in real time and even test multiple scenarios.

These analytics also help to inform smarter opportunity-based sales targets, tailored to each branch’s unique facility characteristics and market dynamics – saying goodbye to blanket goals that are often mismatched with each location’s true potential.

When this information is paired with demographic and business datasets, you’re well on your way to achieving key business objectives like meeting or exceeding annual product level revenue and investment goals, improving branch management, growing or maintaining market share, and improving customer satisfaction thanks to better, data-driven personalization and offers.

Incomplete, inaccurate and little understood data leads to business analytics and reporting that aren’t only flawed, but damaging in the long run – negatively impacting your ability to Know Your Customer (KYC), manage assets, and identify and prevent financial crime compliance (FCC) in real time.

That’s why many financial services companies take action to solve the problem by moving to cloud-native and other data modernization platforms. Data governance has a key role to play here.

As the relationship between business and IT grows, so does the need for complete data catalogs and comprehensive data governance that connects data quality, data integration, and metadata management to accelerate and optimize analytics at all levels of the organization.

A  successful financial services data governance initiative hinges on a business-friendly framework that seamlessly provides enterprise-wide visibility to your data’s source, use, policies, quality, and regulatory requirements. Business and technical users can find, understand, trust, and leverage critical data across the organization – resulting in better KYC outcomes, greater visibility around financial risk and crimes, and improved branch operations.

Data governance is the foundation for enterprise-scale data quality. Both are essential to the other’s success, and to making bigger, better decisions based on data you can trust.

That’s why it’s essential to employ a solution integrated with industry-leading data quality solutions – spend less time doubting your data, and more time bringing the business to new heights.

Across the financial services field, organizations are striving to better understand their customers, market trends, investment opportunities, and new initiatives that may impact profitability, like the growth of ESG.

Trusted analytics and predictive data models can make all the difference in reaching a peak level of understanding, but they require accurate, consistent, and contextual data. Even with their vast amounts of internal business data, financial services institutions are also looking to trusted third-party data to fuel analytical models with new and relevant attributes to gain meaningful insights.

With over 9,000 data attributes describing the people, places, and a myriad of details for any given location, financial services companies will be well-positioned to get the information they need to drive the business forward with confidence and stay ahead of the competition.