Mo’ Data, Mo’ Problems:
Choosing the Right Data
Why the right data, not more data, is key to optimizing your data strategy
Big data and the term ‘data strategy’ gets thrown around a lot – but what is a data strategy when it comes to financial services, and how can you optimize it for more accurate, smarter risk decisioning? The answer isn’t more data, it’s the right data. Read on and discover how to choose the right data for your business use case and why optimizing your data strategy is key to your decisioning success.
Make Your Data Work Smarter, Not Harder
In our increasingly digital society, it seems like everyone is focused on more. More data, more choice, more speed, more competition, more options (how many different entertainment streaming services are there now??). More, more, more. So, our rebel yell is that when it comes to your data strategy, it’s not about more data, it’s about the right data, at the right time. According to IDC, “this year alone, over one hundred thousand exabytes of data will be generated, crossing the 100k threshold for the first time.” Yet 74% of decision-makers we surveyed said they struggle with their organization’s credit risk strategy because data is not easily accessible. The data is there, but it’s an incredible amount of wasted effort if you don’t know which data sources to use when.
74% of decision-makers struggle with their organization’s credit risk strategy because data is not easily accessible.
What can the right data do for your decisioning strategy?
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Improved customer experience: Ensure a frictionless digital experience for low-risk customers and enable data-driven actions on potential risk in real-time
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Improved accuracy in your decisioning: The right data at the right steps in your decisioning processes across the customer lifecycle means more efficient, accurate risk decisions
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Minimized data costs: Reduce the time/effort/resources necessary to source, build and maintain data integrations if all the data you need is right at your fingertips
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Scalability: With the right data sources on both a local and global level, you can get new products to market in new regions faster by duplicating and iterating your data strategy
Types of data that are critical to optimizing your decisioning strategy across the lifecycle:
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Identity Data: Verify identities and documents for better onboarding compliance, prevent identity fraud, and be sure that you are protected with ongoing due diligence data.
Includes: KYC/KYB, PEPs/sanctions, document verification, synthetic ID fraud
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Fraud Data: Identify potential first-party and application fraud in real-time to proactively detect/prevent fraud and reduce losses; reduce false positives by leveraging signals from mobile, email, behavior, device, IP, social and other fraud data sources.
Includes: Email and mobile data, global fraud intelligence, social validation, device data, IP, and geolocation
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Credit Data: Minimize credit exposure and loss by leveraging credit bureau, open banking, and alternative data sources. Ensure optimized credit onboarding and add value throughout the entire customer lifecycle with dynamic customer risk profiling, mitigate collections and optimize customer lifetime value.
Includes: Credit bureau data, business data, open banking and alternative data including social media, rental payments, travel info, utilities and more
Data supply chain challenges and how to overcome them
Choosing the right data can seem daunting, but it’s critical to have an optimized data supply chain, with the right data in the right place, in order to deliver the most effective products to your customers. And depending on the type of financial product you are offering there are regional regulations to consider, third-party vendors required, technology requirements and more. These are some of the most typical challenges known to slow down deployment of even the most well-thought-out data strategies:
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Identifying relevant local data sources
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Negotiating multiple contracts
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Complying with varying regulations
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Ensuring data privacy for different regional requirements
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Normalizing data formats
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Building and maintaining integrations
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Supporting global strategies
But you can overcome these challenges by ensuring you have the right data for each and every product offering you have. How? Work with a partner that provides an all-in-one data solution. Building your own data supply chain, for whatever your use case, is possible of course, but it’s time-consuming and resource intensive. If you want to work with a partner look for a data solution that offers:
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One data contract that provides access to multiple data sources
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A single API to replace numerous integrations
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A wide variety of data types and sources, including alternative data
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Expert data source curation customized to your needs, that can be easily modified as your needs evolve
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Simplified, no-code data supply chains that non-technical users can understand and control
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Global data access, as well as local sources, to ensure success of both regional tactics and the ability to iterate and expand to new markets
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Seamless integration into your decisioning technology to ensure accurate, smarter decisions
If you’re a Buy Now, Pay Later provider, or are thinking of diving into the fray, check out our blog highlighting specific ways to optimize your data supply chain for BNPL.
Read the Blog
Simplifying your data supply chains (sourcing, building, integrating, and maintaining data sources and connections) and optimizing your data strategy is critical to continued success – and your competitive advantage. Don’t let yourself be overwhelmed by the immense variety of data available out there – remember, the right data is much more important than more data. Accessing the right data at the right time means enhanced risk models, strengthened onboarding processes, more accurate decisioning across the lifecycle, and optimized customer experiences.
For further reading, check out these articles that may be of interest:
What the Data-Driven Bank of the Future Looks Like
– The Financial Brand
Designing Next-Generation Credit-Decisioning Models
– McKinsey
The Data-Driven Enterprise of 2025
– McKinsey
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