6 Ways Fintechs Use Big Data

Big data refers to the collection, consolidation, and analysis of information that has one of the following characteristics:

  • High Volume
  • Rapid Acquisition
  • Extreme Complexity

In other words, big data is the process that allows technology to provide insights using numerous data points that are impossible for humans to process effectively. 

The financial sector has always collected and stored data. Think of the information banks collect for loans. Insurance carriers know a lot about homes and cars. Investment firms have details about life plans.

Bringing that information together allows companies in the fintech industry to gain a comprehensive picture of customers.

Fintech’s focus on technology uses big data to fuel decisions. Sometimes, big data combines with artificial intelligence (AI) to deliver financial services.

Using technology has allowed fintech to disrupt the financial services sector to deliver more customized products and personalized services. Whether it’s fraud protection or financial advice, fintechs have changed what people expect from the financial services sector. 

Let’s explore ways fintech uses big data to disrupt the financial services sector.

Fraud Protection

fraud illustration

Consumers hold financial services providers responsible for card-based fraud. Almost 30% would change providers if fraud protection were poor. Given that at least 10% of all consumers have experienced fraud in the last year, the potential for customer dissatisfaction is significant.

Fintechs leverage big data to help detect and prevent fraud. For example, suppose I was on vacation in Antigua, where I had used my debit card several times when someone used my card at a toy store in California.

The card issuer flagged the California transaction and froze my account. Granted, the bank prevented a fraudulent transaction, but it also prevented me from using my card on vacation.

Today, big data could change that process. Instead of following a set of rules based on the assumption that I couldn’t be in two places simultaneously, the fraud detection technology could evaluate years worth of data to know that I vacation in Antigua frequently, rarely purchase toys, and live in Chicago.

The system could deny and flag the fraudulent transaction without freezing my account. It could notify me via email or text of the potential fraud and ask for confirmation that it was an invalid transaction request.

Predictive Analysis

predictive analysis

According to Gartner, predictive analytics are used to predict possible outcomes over time through simulation. Using data- and time-based forecasting, financial institutions can determine the likelihood of different outcomes. Fintechs can leverage their cloud-based infrastructure to analyze internal and external data sources.

Investment fintechs can use big data to assess market volatility. They can collect data from European, North American, and Asian markets to determine the likelihood of market disruption. They can make investment recommendations based on the analysis. They can even look at geopolitical factors that might impact investments. 

Companies can compare their recommendations to their clients’ risk-averse scores to determine which investments to recommend. The predictive capabilities help fintechs provide personalized services for their investment clients. Many traditional investment firms like Vanguard use big data to fuel their fintech offerings.

Customer Experience

customer experience

Improving the customer experience is a key use of big data in fintech. Consumers aren’t thrilled with traditional financial services and are turning to online solutions for managing their finances. Fintechs use big data to provide a personalized, not just a customized, experience. 

Financial institutions customize experiences based on personas or profiles. For example, couples between the ages of 35 and 55 who own a home that is at least 15 years old may be interested in a home equity line of credit. Institutions send out emails or display pop-ups to attract the target customer base. 

Fintechs can dig deeper. They can extract data that delivers a more personalized experience. Big data can look at buying history to find an uptick in home improvement items or an upcoming anniversary. They can then personalize the offers to discuss home renovations or celebrating a 25th wedding anniversary. This extra touch makes consumers feel less like a number and more like an individual.

Service and Products

service and products mobile banking

Chatbots are another customer-facing technology that relies on big data and AI for improved customer experiences.

Data scientists create algorithms to analyze data that chatbots or roboadvisors can use to suggest products or services to customers. For example, online users want to open accounts. Chatbots can be used to collect information and recommend the best accounts for their financial needs. 

More online investment firms use roboadvisors to help clients select financial instruments for investing. They may look at existing portfolios and age to recommend a more balanced investment landscape. From the data, the roboadvisor may determine that a customer is environmentally conscious and direct them toward companies with a strong environmental, social, and governance rating (ESG).

Because many fintechs can aggregate financial information from multiple sources, they provide a more comprehensive analysis of an individual’s financial health. When these sites make recommendations, they make them from a larger data set, resulting in more accurate suggestions. 



Banking plays an essential role in a country’s economic infrastructure. Failures in the financial sector can have catastrophic consequences, such as the Great Recession and the Great Depression. Both of these events resulted in stricter regulations to prevent repeat occurrences.

As a result, the financial sector is one of the most highly regulated industries with reporting requirements to prevent activities such as money laundering and tax fraud.

Big data helps fintechs collect data needed to meet reporting requirements and flag questionable activities. A recent change to the foreign investment act requires foreign investments in US companies to be reported if they are substantial. Before the change, reports were required only if the investment resulted in controlling interest.

Fintechs that collect transaction data can use the information to produce the required reports. Whether it is foreign investment data or high-value money transfers, big data can assess the transaction in real-time and flag it for later reporting. Streamlining the reporting process improves operational efficiencies and minimizes the risk of becoming out of compliance.

Risk Assessment

risk assesment

Mitigating risk is a foundational requirement of a financial entity. Will the loan be repaid? Should someone in a high-risk profession be insured? What about homes in a floodplain? All of these concerns can be addressed using big data. 

Big data enables fintechs to evaluate lending criteria quickly. Cloud-based computing capabilities allow fintechs to analyze large amounts of data in real-time They can review credit reports, financial transactions, and lifestyle data to decide if a loan or insurance policy is low-risk. 

Traditional processes require the information to go through underwriting, which can take days to complete. With big data, fintechs create algorithms that evaluate the data and assess risk. They can perform this assessment for loans, insurance, and investments. The process can use the same criteria as an underwriter; it is just completed faster and with more data.

The Bottom Line

Within the financial sector, many analysts are suggesting institutions move from customer experience to customer science. For fintechs, that move has already happened with the use of big data.

Customer science predicts what clients will do before they do it. They look at behaviors reflected in data from multiple sources. Then they deliver a customer experience designed for the specific individual.

Fintechs can analyze spending behaviors and recommend savings methods that work for each individual. They can find ways to provide value. Fintechs can also use big data to minimize risk. If life insurance applicants indicate they do not participate in high-risk activities, big data can check social media sites and purchase history to see if they sky-dive, mountain climb, or scuba dive.

Big data is not just collecting numbers from spreadsheets and information from databases. It also involves extracting data from images, videos, and social postings. Expanding data collection to nonstructured data provides fintechs with access to untapped resources for delivering financial services.