Data Enhancement turns raw, inconsistent transaction strings into clean, categorized, merchant-identified data that every product, team, and AI initiative trusts.
Raw strings go in. Clean, categorized, merchant-identified, understood data comes out, ready for every product, team, and AI initiative. Each capability, with the proof it earned.
Every transaction lands from a core system or an aggregation feed as a raw string: truncated, inconsistent, unstructured. No merchant name, no reliable category, just noise.
Fewer disputed transactions, alongside an 8% reduction in call center costs. Forrester TEI of MX
Accurate categories reflect how consumers actually spend and earn. That is what makes budgeting tools trustworthy, analytics segments precise, and personalization land instead of miss. Your team customizes data cleansing to match its needs.
MX resolves the merchant behind the string and adds the logo, website, and location, across common and long-tail merchants alike. Consumers recognize a charge at a glance instead of calling to ask what it is.
Recurring interchange from a single one-month campaign, with 25,000+ new recurring payments added. Regional bank, $50 billion
MX flags recurring bills and subscriptions and identifies the employer behind a payroll deposit. That signal layer sits under cash-flow guidance, loan qualification, income verification, and direct deposit targeting. The growth motions that depend on knowing a consumer's income and obligations finally have the data to run on.
Higher deposit switch conversion versus leading industry benchmarks, powered by employer identification. MC Client Result
Data Enhancement is not one product in the platform. It is the layer every other product runs on. Clean the data at the source and every connected product gets more accurate at once, without touching each one individually. No point enrichment vendor makes that claim.
Every segment, audience, and profile is only as accurate as the transaction data feeding it.
Proactive, behavior-based guidance depends on cleansed and enhanced data to be right.
Accurate categorization and merchant identification make budgeting tools trustworthy.
Enhanced transaction data underlies every consumer-facing experience in the app.
Employer identification powers the payroll targeting that drives switch campaigns.
It keeps getting more accurate on its own. The models are built on years of real financial transactions and improve through daily automated monitoring and monthly human-verified audits, so accuracy tracks merchant behavior and transaction patterns as they evolve. The FI does not own the maintenance burden.
Transaction data arrives from the FI core system or aggregation as raw, inconsistent strings. 100% of it flows through the MX Data Engine.
ML, NLP, rules-based logic, and proprietary financial LLMs turn each string into a clean, categorized transaction.
Add the merchant, logo, location, recurring pattern, and employer, so the data explains what happened, not just that it happened.
Consume it via widget or API, and it feeds Customer Analytics, Insights, Financial Wellness, Mobile, and Direct Deposit Switch natively.
Case Study
By pairing data aggregation and AI with real-time personalized insights, OneUnited Bank deepened engagement and helped customers build balances.
"By utilizing data aggregation and artificial intelligence to provide real-time personalized financial insights, OneUnited Bank and MX are fulfilling the promise of economic growth that comes from creating a more financially literate society."
Kevin Cohee, Chairman & CEO, OneUnited Bank
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Case Study
Evansville Teachers FCU used MX Insight & Target to spot card-ready members and move spend back on-us, multiplying balance transfers and new-card acceptance.
"It's a testament to the information that we're able to get out of Insight & Target, and it's so very important to the service, to use that data to help our members, save them money, and move them toward our products and services."
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Case Study
Central Pacific Bank embedded MX-powered direct deposit switching into its mobile app, turning paycheck movement into a repeatable growth motion.
"We embedded our direct deposit switch into the mobile app, and the results have been unbelievable. The day we put it on a mobile device, we had 60 conversions in the first week."
Jason Lazzerini, EVP & Chief Digital Officer, Central Pacific Bank
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Case Study
A regional bank used MX enhanced transaction data to target a one-month recurring-payments campaign, adding recurring interchange revenue.
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Talk to MX about the shared data foundation that makes every product, team, and AI initiative more accurate.
Gambling activity shows up in transaction data, and it's a financial-health signal FIs can't ignore.
Agentic AI is only as good as the data under it. Clean, enriched data is what makes it work.
Every insight and every experience starts with a reliable, well-structured connection to financial data.
Stronger engagement drives stronger financial wellness, and the data shows how the two reinforce each other.
What consumers care about is shifting fast. Here's what the latest MX research shows.
What Formula 1 reveals about winning in financial services: precision, speed, and clean data.
What data and digital teams ask before they build or buy.
A transaction data enrichment API takes the raw string a bank core or an aggregation feed produces and returns it as structured data. The raw string is truncated and inconsistent, with no reliable merchant and no reliable category. The enriched record adds a clean, human-readable description, a category, the merchant behind the charge with its logo and location, and signals such as whether the charge recurs. MX Data Enhancement is that layer, and you consume it through a RESTful API or inside MX's own experience layer.
You resolve the merchant behind the string rather than trying to tidy up the string itself. MX runs every transaction through machine learning, natural language processing, rules-based logic, and financial language models trained on real transaction data. It then matches the charge to a known merchant and attaches the name, logo, website, and location. Common and long-tail merchants both resolve. The result is a charge a consumer recognizes at a glance, which is what stops the "what is this" call and the false fraud dispute.
The honest answer is that it depends on what you plan to do with the data. If you only need cleaner statement lines, a point enrichment vendor will do. If the enriched data has to feed analytics, financial wellness, mobile, and campaign targeting, judge an API on three things. Does it identify the merchant behind a charge or only its category? Does it detect recurring payments and payroll employers? Does the same clean record flow to every product you already run? MX cleans the data once at the source, so every connected MX product gets more accurate at the same time. No point enrichment vendor makes that claim.
Accurate transaction categorization makes budgeting tools and spend breakdowns trustworthy enough for consumers to rely on. Recurring and employer signals feed cash flow underwriting, so your underwriter reads real cash flow instead of a short credit history. Knowing which recurring charges sit on another institution's card is what makes a top-of-wallet campaign targetable.
Have other questions? Get a demo.
Independently audited controls for security and availability
Card data handled to payment-industry standards.
A public report you can read without an NDA
Aligned with California consumer-privacy rules.
Aligned with EU data-protection requirements.
