Financial Technology

Fraud losses down.
Retention up.

AI credit risk and fraud detection models built on unconventional data, replacing static underwriting with real-time decisioning across the loan book.

350+ strong team
built from scratch
Real-time credit risk
and fraud detection
Unconventional
data
and customer genomics
in every model
AI-optimised pricing
and retention

Client

A top AI-driven financial services company, with credit risk and fraud detection at the core of its business.

Goal

Build next-generation AI models for credit risk and fraud detection, using unconventional data sources to sharpen underwriting and cut fraudulent activity.

Risk models weren’t built for real-time decisions.

Creditworthiness and fraud were being assessed with tools that couldn’t keep pace with real-time transactions. The business needed next-generation credit risk models that could make real-time creditworthiness and fraud calls, not backward-looking ones.

Pricing strategy and customer retention were tied to the same weak signal. Without better predictive insight, the company was leaving financial performance and retention on the table alongside the fraud exposure.

Three constraints were non-negotiable:

  • Real-time creditworthiness and fraud detection
  • Pricing strategy tied to financial performance
  • Retention dependent on predictive accuracy

Models built on data competitors weren’t using.

AI-driven credit risk models were developed using unconventional data and customer genomics, sharpening underwriting accuracy beyond what standard credit data could deliver.

Customer genomics and unconventional data went into every risk score, not just credit history.

Alongside this, AI-powered fraud detection systems were designed and implemented using behavioural and transaction data, catching fraudulent activity as it happened rather than after the fact. Pricing strategy was optimised on top of the same models to boost retention and profitability together, rather than trading one off against the other.

None of this ran on a small team. A 350+ member team was built and scaled across data science, engineering, and decision science to drive the enterprise AI initiative end to end.

Sharper risk models, safer transactions, stickier customers.

Risk assessment streamlined Credit risk processes became faster and more efficient, cutting the operational load of underwriting.
Fraud losses reduced AI-driven detection systems improved transaction security and cut fraud losses directly.
Retention boosted AI-optimised pricing and personalised recommendations lifted both retention and profitability.
350+ team built A data science, engineering, and decision science team was built and scaled to run the initiative long-term.

Real-time credit risk and fraud detection models, built on unconventional data and run by a 350+ strong team, turned underwriting, fraud prevention, pricing, and retention into one connected system instead of four separate problems.

* Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.

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*Case studies reflect work undertaken by our Heads of AI either during their tenure with Head of AI or in prior roles before they were part of the Head of AI network; they are provided for illustrative purposes only and are based on conversations with our Heads of AI.