Data Quality

Leveraging data for investment and market intelligence - FinTech solutions

The financial services industry has been around since time immemorial. Which means Fintech start-ups face the challenge of proving the value of their products to industry giants, who may be set in their ways. Our expert, Carson Collins, explains how FinTech companies can put data to work to improve their products and prove their value to prospects.

Meet the expert
Carson Collins
Carson Collins
Account Manager

Carson Collins has spent the last 5 years building his career in data licensing with Data Axle. Carson understands his client's challenges and is passionate about customizing solutions to best fit their needs and helping them exceed their business goals.


How can financial services companies put third-party data to work to increase revenue, customer acquisition/retention and manage risk?

a. Analyzing Historical Trends – FinTech companies can use historical data to evaluate the performance of companies, industries, and markets over time in order to better predict future outcomes. For example, for a FinTech company that needs insight on positions to take for companies with large physical retail presences (Yum! Brands, O’Reilly Auto Parts, GameStop), building real-time business location data into models can be a means to forecast longevity and performance based on prior outcomes.

b. Performing Market Analysis – Market analysis allows FinTech companies to identify new market opportunities, develop correct, non-consensus views and do their homework on industries and companies. For example, a company can find comparable companies, determine performance, and market opportunity by region, industry, employee size, etc. Leading banks and investment firms rely on these findings to inform their customers about potentially undervalued areas and themes.

c. Improving Fraud & Risk Detection – In 2019, The Financial Cost of Fraud Report, developed by audit, tax, advisory and risk firm, Crowe, estimated that fraud cost businesses and individuals $5.127 trillion dollars globally each year. FinTech companies are using data to detect suspicious activity – allowing their clients to take proactive measures to keep these costly incidents from occurring. For example, FinTech companies are now using online and offline details of an individual or business such as geographic locations, phone numbers, and email domains to authenticate a user’s identity.

d. Commercial Loan Underwriting Optimization – Information is an essential part of effective decision making. Small business lending is no different. Many traditional methods of assessing credit worthiness and estimating business resilience have become antiquated, and this has created room for more data-driven loan underwriting. When new market conditions arise, alternative, real-time data can be critical for navigating these new conditions. For example, evaluating credit worthiness amid a global pandemic has been a challenge for lenders as past “rules” have shifted. Many businesses have outperformed baselines due to exponential demand while many others have struggled to get customers in the door. Third-party data can shine a light on these new patterns. Lenders are now using factors like how long you have in business, revenue and cash flow, industry, employee size, bankruptcy, and personal information to make decisions and create risk-based rates.


How can FinTech companies use third-party data to transform the customer experience?

Banks are always looking for ways to make the online consumer banking experience better. Historically, when they have presented customers with their transactional spending details, it has been nothing more than a list of what vendors they transacted with, the date, and the amount of the transaction. FinTech companies have stepped in to make that data more compelling. They are relying on third-party data to provide enhanced intelligence by cleansing and categorizing beyond the baseline transaction exhaust data. So, instead of simply saying a customer spent 100 dollars at x business and 50 dollars at y business, they can provide a detailed breakdown of spending by category. For example, the customer spent 5% on gas, 15% on clothing, 20% on childcare, etc. This data is helpful for budgeting and allowing users to get a grasp on how their money is being spent.

Another example of improving the customer experience is the use of third-party data to simplify the form filling process for products such as commercial loans, commercial insurance, etc. Rather than relying on manually entered data – such as address, employee size, business description and other details – an automated form pre-fill can complete that process with the click of the mouse. The net result of this is a reduction in application abandonment, and inaccurate data due to human error and improved customer satisfaction.


What type of firmographic or demographic data is crucial to building financial services products?

a. Historical Trends and Market Analysis Usage – Employee Size, # of locations, Business Categories (SIC/NAICS), Address (lat/lon).

b. Fraud & Risk Detection Usage – Address, Contact Information, Emails, Phone Numbers.

c. Commercial Loan Underwriting Optimization Usage – Legal Name, Address, Phones, Employee Size, UCC Filings, Years in Business.


What are the various ways that companies can access and use this data?

a. APIs – Data Axle provides real-time data access via a suite of APIs. These APIs allow users to match, append, search and capture important data changes.

b. File Deliveries – Data Axle can provide file deliveries on predefined sets of categories, companies, regions and more. This data can be delivered on whatever cadence meets the client’s needs and use case. In some cases that might be monthly. In other cases, it may be as frequently as daily.


What to look for in a data provider:

FinTech companies should look for these three things when selecting a third-party data partner:

a. Breadth & Depth – Data breadth & depth helps ensure data-driven decisions are not made based on an incomplete picture of the business landscape.

Questions to ask data providers: What is the coverage of the data? Does the dataset track every business regardless of industry? Does the dataset track every business regardless of size? What attributes can be layered on for deep insights about a business (corporate structure, years in operation, historical employee size)?

b. Accuracy – Data accuracy helps ensure decisions are not made with an incorrect picture of the business landscape.

Questions to ask data providers: Where is the data sourced from? What quality checks help maintain the dataset?

c. Recency – Companies in the FinTech industry are making decisions within fractions of a second. Making sure the third-party data you acquire is available and updated in real-time ensures you’re not making decisions based on an outdated picture of the business landscape.

Questions to ask data providers: How frequently is the data updated? Can it be accessed in real-time?

Want to learn more about how to put your data to work? Contact us.

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