A loan application looks like a form. To Credolab, it looks like a choreography. There is the pace of a person's typing, the order of their taps, the settings on the device, the pauses before a choice and the way a cursor moves through a page. None of those details is a credit history. Together, the company argues, they can become a useful signal about risk, intent and whether the applicant is even human.
That distinction is the foundation of a ten-year-old fintech business. Founded in Singapore in 2016, Credolab makes software for banks, neobanks, card issuers, consumer lenders, buy-now-pay-later providers and retailers. Its lightweight software development kits sit inside a client's mobile app or website. With consent, they collect anonymized device and interaction metadata. Machine-learning models turn those observations into scores and insights, delivered through an API while the application is still underway.
The customer never downloads a Credolab app. There is no consumer dashboard to admire. The product lives behind somebody else's “Apply” button, supplying a quiet second opinion when a bureau file is sparse, an identity check is expensive or a polished application feels a little too perfect.
The bureau sees yesterday
Conventional credit underwriting is built around recorded history: loans opened, payments made, balances carried and obligations missed. It is powerful when the record exists. It is less useful for a young borrower, a recent migrant, a cash-heavy worker or anyone living in a market where bureau coverage is incomplete. “Thin file” sounds clinical, but its practical consequence can be blunt: the system has too little information, so a potentially good customer is treated as an unknown.
Credolab sells a way to make that unknown smaller. Its material is first-party metadata gathered during a digital journey rather than purchased from a data broker. The company says it does not capture names, messages or other personally identifiable information. Instead, its SDKs observe categories such as device settings, browser attributes, gestures, interface interactions and typing cadence. The model asks whether those patterns, tested against a lender's outcomes, improve prediction.
Behavioral metadata
- Typing cadence
- Gestures and UI paths
- Device and browser settings
- Session integrity
Decision signals
- Credit-risk score
- Fraud alerts
- Income prediction
- Marketing insights
This is not meant to evict the credit bureau. Credolab's more credible pitch is to complement it. A lender can run the new score beside its existing model, compare results in a champion-challenger test and decide whether the extra layer improves the Gini coefficient, approval rate or bad rate. After enough performance data arrives, Credolab can calibrate a tailor-made scorecard to the institution's risk appetite.
Easy access to fair credit for all.Credolab's published vision
A filter before the expensive filters
The same behavioral layer can solve a different problem at the top of the funnel. Digital lenders now face bots, emulators, device farms, synthetic identities and people who present a clean conventional profile but never intend to make the first payment. Identity verification can answer who a person is. A bureau can describe what that person has done. Credolab tries to add how the current session behaves.
Placed early in onboarding, the software can flag non-human or manipulated activity before a lender pays for deeper identity, income and compliance checks. It can also reduce false positives by giving a legitimate but unfamiliar applicant another signal in their favor. The commercial logic is simple: spend the costly checks on sessions that look genuine, investigate the suspicious ones sooner and avoid rejecting every applicant the bureau cannot describe.
Partnerships make that position clearer. Credolab complements TransUnion's TruValidate fraud network, participates in Mastercard's Engage partner network, and integrates with Provenir and HES FinTech decisioning systems. Its 2025 partnership with ZOLOZ combines identity verification with device and behavioral intelligence. Michele Tucci, Credolab's co-founder and chief strategy officer, described the two layers neatly: one establishes who the user is; the other studies how the user behaves.
What buyers actually buy
Credolab's current menu mixes subscription software with applied modeling work. Credo Lite includes mobile or web SDK access, a dedicated cloud environment, dashboard or API credentials, monthly behavioral reporting, and staff training. Credo One adds device, fraud, app and behavioral intelligence. Credo Score moves into custom territory: the company develops and calibrates a risk scorecard on the client's own performance data, then serves production scores through an API.
The public annual prices are unusually legible for enterprise risk software: $4,485 for Credo Lite and $7,485 for Credo One, each covering up to 100,000 uploads, with custom scoring sold through consultation. The documentation lists SDKs for Web, Android, iOS, React Native, Flutter, Cordova and Capacitor. In plain terms, Credolab charges for the embedded collection layer, the stream of scored insights and, at the higher end, the statistical labor required to make those signals fit a particular loan book.
Risk is still the center of gravity, but the product line has widened. A fraud intelligence suite arrived in 2020. Marketing intelligence followed in 2022, using behavioral features for segmentation and engagement. In November 2025, Credolab introduced an income prediction model designed to estimate an applicant's income level when verified income data is unavailable. The underlying maneuver stays consistent: turn the digital interaction itself into a predictive feature set.
The proof is local
Alternative data does not earn trust because it sounds novel. It earns trust when it survives out-of-sample testing, remains stable across time and adds information after the lender's existing variables are considered. Credolab publishes a range of anonymized case studies and several named customer stories. A Philippine BNPL model, for example, showed a 37.5 percent Gini improvement in an integrated model. Other published cases describe lower bad rates or wider approved populations.
Those numbers should be read as portfolio results, not universal promises. Performance varies with country, product, customer mix, target definition and the quality of the lender's baseline model. That is why the company's validate-then-calibrate process matters more than any single percentage. A signal that travels globally still has to prove itself locally.
Does the behavioral layer add predictive lift after bureau and identity data, remain explainable to risk teams, preserve consent, and hold up on customers the model has never seen? The answer belongs in the validation report, not the sales deck.
Privacy is the other non-negotiable. Behavioral analytics can sound uncomfortably close to surveillance when described carelessly. Credolab's answer is architectural: active consent, anonymized metadata, no personal or sensitive content, and no purchased third-party data. Its trust center says legal and regulatory assessments are part of the product lifecycle, and the company reported renewed ISO 27001 certification in 2024. Lenders still carry their own duty to explain the data use, assess fairness, monitor drift and give applicants the rights required in each market.
A quiet place in a noisy market
The competitive field stretches in several directions. Experian, Equifax and TransUnion own the traditional record. Nova Credit and cash-flow underwriting providers translate other financial histories. Socure, Alloy, Sardine and SEON concentrate on identity and fraud. Decisioning platforms such as Taktile and Provenir help institutions orchestrate the rules and models. Credolab occupies a narrower slot: proprietary behavioral data collected through its own embedded SDKs, supplied as another input to systems a lender already uses.
That focus explains the company's partnerships and its low public visibility. Credolab says many large clients prefer not to disclose the scoring partner behind their approval funnel. Others do: its site names companies including FairMoney, Agibank, alt.bank, Lucky and CrediOrbe, alongside partnerships with Círculo de Crédito and TransUnion. The company reports 325-plus clients on its current corporate page, 200-plus million people scored and more than one billion datasets, though a late-2025 release used the more conservative description of nearly 100 clients.
The company itself has moved with the market. After beginning in Singapore, it opened a Miami hub in 2020 and a Dubai hub in 2022. A $7 million Series A led by GBG in 2020 supported expansion through East Asia, Latin America and Africa. Credolab says it reached profitability in 2023. By its account, Latin America supplied nearly half of revenue by 2024. Its roughly 35-person scale, based on the supplied company record, makes the global footprint look less like a branch network and more like a distributed specialist team.
The useful second opinion
The most interesting thing about Credolab is not that a phone can produce thousands or millions of features. Modern software can produce features from almost anything. The useful idea is narrower: a digital journey may contain enough fresh context to separate “we do not know” from “this looks risky,” without reading a person's private content.
For a lender, that can mean approving a worthy applicant who would otherwise disappear into the thin-file pile, catching a fraudulent session before paying for every downstream check, or learning which customers need a different message. For the applicant, the technology is valuable only when that extra signal creates a fairer path rather than a new opaque gate.
Credolab's decade has been spent in that tension. It is a scoring company arguing for more information while promising to know less about the person. The product works best as a second opinion - fast, embedded and tested against reality - while the final judgment remains with the institution that chooses to listen.