Plaid connects over 12,000 financial institutions to more than 8,000 fintech applications. When a customer links their bank account to Venmo, Robinhood, or Coinbase, Plaid’s infrastructure pulls transaction data from the bank, normalises it into a consistent format, categorises each transaction, and delivers structured data to the application. The raw bank data is messy: different banks use different transaction codes, different description formats, and different categorisation schemes. Plaid’s data intelligence layer transforms that mess into clean, standardised information that fintech applications can use to build products. Without that intelligence layer, every fintech company would need to build its own bank data integration for each of the 12,000 institutions. Plaid’s $13.4 billion valuation (at its 2021 funding round) reflects the value of solving data intelligence at infrastructure scale.
Data intelligence, the ability to collect, structure, interpret, and act on data, is the capability that separates fintech companies that grow from those that stall. According to MarketsandMarkets, the global AI in finance market reached $38.36 billion in 2024 and is projected to grow to $190.33 billion by 2030. AI and machine learning are the tools. Data intelligence is the capability those tools create. The distinction matters because AI without good data produces nothing useful, while good data infrastructure makes every AI application more effective.
What Data Intelligence Means in Practice
Data intelligence is not a single technology. It is an organisational capability with four components.
The Boston Consulting Group projects fintech revenues will reach $1.5 trillion by 2030, with embedded finance and digital lending accounting for the largest share of projected growth.
According to CB Insights’ 2024 fintech report, global fintech funding declined 40 percent between 2022 and 2024, pushing the sector toward consolidation and a sharper focus on profitability over growth at all costs.
Data collection captures information from every relevant source: customer transactions, app interactions, market feeds, regulatory databases, and third-party data providers. The scope of collection determines the ceiling on what a company can learn. A fintech company that captures only transaction amounts and dates has less intelligence potential than one that captures transaction amounts, dates, merchant names, merchant categories, device information, location data, and session behaviour.
Data structuring transforms raw information into formats suitable for analysis and machine learning. Financial data from different sources arrives in different formats, with different naming conventions, different levels of completeness, and different update frequencies. Structuring this data into a unified, consistent format is where most of the engineering effort goes. MX, a financial data company, built its business around structuring bank data for financial institutions. The company cleanses, categorises, and enriches transaction data so that banks and fintech companies can build products on reliable information.
Data interpretation extracts meaning from structured data. This is where machine learning, statistical analysis, and business logic convert raw numbers into actionable intelligence. A transaction record showing a $47.23 charge at “SQ *COFFEE SHOP NYC” is raw data. Interpreting it as a food and beverage purchase at a specific merchant in a specific city, comparing it against the customer’s typical spending pattern, and flagging it if it exceeds normal behaviour turns raw data into intelligence.
Data activation uses intelligence to drive decisions and actions. A credit model that uses data intelligence to approve a loan, a fraud model that blocks a suspicious transaction, a personalisation engine that recommends a savings product, these are all examples of data activation. The value of data intelligence is realised only when it changes a decision or triggers an action.
Why Data Intelligence Determines Competitive Position
In fintech, data intelligence creates competitive advantages through three mechanisms that compound over time.
Better products. Fintech companies with superior data intelligence build better products because they understand their customers more deeply. Revolut’s spending categorisation, budget recommendations, and savings suggestions are powered by data intelligence that analyses each customer’s transaction patterns. The product feels personalised because the data intelligence behind it treats each customer as an individual, not a segment. Companies with weaker data intelligence offer generic products because they lack the information to personalise.
Lower costs. Data intelligence automates decisions that would otherwise require human judgment. Nubank serves 100 million customers with approximately 8,000 employees because data intelligence automates credit decisions, fraud detection, customer service routing, and compliance monitoring. A traditional bank without this capability employs five to ten times more people to serve a comparable customer base. The cost difference flows directly to the bottom line and allows fintech companies to offer lower prices or better terms than traditional competitors.
Faster improvement. Companies with strong data intelligence improve their products faster because they can measure the impact of every change. When Chime tests a new savings feature, data intelligence measures adoption rates, usage patterns, and financial outcomes within days. If the feature works, it is rolled out broadly. If it does not, it is modified or removed. This rapid feedback loop means that products improve continuously rather than being updated on quarterly or annual cycles.
Data Intelligence in Action: Four Case Studies
The following examples illustrate how data intelligence creates measurable business outcomes across different fintech categories.
Stripe’s network intelligence. Stripe processes payments for millions of merchants across 195 countries. The data intelligence from this network is the foundation of multiple products. Stripe Radar uses network-wide transaction data to detect fraud patterns that no individual merchant could identify. Stripe Capital uses merchant transaction data to make lending decisions. Stripe Revenue Recognition uses payment flow data to automate accounting. Each product draws from the same data intelligence layer, and each product’s usage generates data that improves the others. The network intelligence compounds with every new merchant that joins the platform.
Adyen’s merchant analytics. Adyen processes payments for enterprise merchants including McDonald’s, Spotify, and eBay. The company uses data intelligence to optimise payment routing, selecting the processing path most likely to result in authorisation for each specific transaction. A one-percentage-point improvement in authorisation rates can generate millions in additional revenue for a large merchant. Adyen also publishes an annual retail report that analyses consumer payment behaviour across industries and geographies, using the same data intelligence that powers its product to produce content that builds the company’s industry reputation.
Ant Group’s financial inclusion. Ant Group’s Zhima Credit scores over a billion users using more than 3,000 variables per person. The data intelligence system evaluates spending behaviour, payment patterns, social connections, and digital activity to produce credit scores for people with no traditional credit history. This data intelligence has extended formal credit access to hundreds of millions of people in China who were previously unservable. The capability is built on data infrastructure that Ant Group has accumulated over more than a decade of operating Alipay.
Square’s merchant lending. Square Loans uses point-of-sale transaction data to assess small business creditworthiness. The data intelligence is specific: daily revenue, transaction frequency, customer return rates, average ticket size, and seasonal patterns. This information is more current and more granular than the quarterly financial statements a traditional lender would require. Square has originated over $17 billion in loans using this data intelligence, with consistently low default rates because the intelligence reflects real-time business health rather than historical snapshots.
Building Data Intelligence Capability
Fintech companies that want to build strong data intelligence invest in three areas.
First, data engineering. The infrastructure that collects, stores, processes, and delivers data is the foundation. Without reliable data pipelines, real-time processing capability, and consistent data quality, no amount of analytical sophistication will produce useful intelligence. Fintech companies typically invest 30 to 40 percent of their engineering resources in data infrastructure.
Second, analytical talent. Data scientists, machine learning engineers, and analytics engineers translate data into intelligence. These roles are in high demand and expensive to fill. Fintech companies compete for this talent against technology giants that can offer higher compensation. The most effective approach is embedding analytical talent within product teams rather than isolating them in a centralised analytics department.
Third, data governance. Grand View Research notes that regulatory requirements around AI governance are increasing, with the EU AI Act classifying credit scoring and risk assessment as high-risk applications. Data governance frameworks ensure that data collection, storage, and use comply with regulatory requirements while still providing the access that models need. Getting governance right is essential because the penalties for getting it wrong (regulatory fines, customer trust erosion, data breaches) can be existential.
The fintech companies that will lead their categories in five years are the ones investing in data intelligence infrastructure today. The models, products, and customer experiences that will differentiate winners from losers all depend on the same foundation: the ability to collect data comprehensively, structure it reliably, interpret it accurately, and activate it in real time. Data intelligence is not a department or a project. It is the core operating capability that determines whether a fintech company can compete at the level the market now demands.
