Scores nobody can explain
A fraud or risk score without reasons is hard for an analyst to act on, and harder to defend to a customer, an auditor or the Financial Ombudsman Service.
AI by industry · Fintech and banking
AI for UK Fintech and Banking Teams
Document AI for KYC and KYB packs, machine learning on fraud and transaction signals, and assistants that answer from approved content — each matched to the job, measured on your own historical cases and designed so an analyst makes the call.
- Every model output logged with its inputs
- The model prepares the case; analysts decide
- Evaluated on your historical cases before go-live
Why finance AI stalls
In regulated finance, a clever model is not enough.
Fintech AI projects rarely fail on accuracy alone. They stall when nobody can explain an output, when labelled data is thin, or when the model’s job overlaps a decision the firm has to own.
Where AI earns its place in finance
The dependable wins come from preparing work for a person, not replacing their judgement:
- Reading identity, company and financial documents into structured fields
- Ranking alerts so analysts see the likeliest cases first
- Enriching raw transactions with clean merchant and category data
- Summarising case history before a call, review or decision
- Answering routine customer questions from approved content only
Few labels, fewer confirmed frauds
Confirmed fraud is rare and often labelled weeks later, so a model trained naively learns the ordinary case and misses the pattern that matters.
Patterns that keep moving
Fraudsters adapt, products change and spending shifts with the economy. A model nobody monitors for drift quietly gets worse while its dashboard still looks green.
Decisions the firm must own
Credit, onboarding and account closures have real effects on people. Handing them to a model without safeguards creates data protection and fair-treatment risk.
What we build
AI techniques, matched to finance jobs.
Each job gets the simplest technique that works — rules where rules suffice, classic machine learning for scoring, language models for reading and drafting — with an evaluation set built from your own cases.
Identity and company document reading
OCR and document models read passports, licences, bank statements, incorporation certificates and accounts, returning each field with a confidence level for analyst review.
Document processing
Fraud and anomaly scoring
Gradient-boosted and anomaly-detection models on payment, device and behaviour signals, working alongside your existing rules, with the top reasons listed next to every score.
Predictive analytics
Transaction enrichment
Clean merchant names and assign categories to card and Open Banking transactions, so budgeting features, affordability reviews and reporting work from tidy data.
Discuss this
Policy and procedure search
Retrieval over internal policies, product terms and procedures, so operations and compliance staff get cited answers instead of hunting through a wiki.
Knowledge search
Service assistants with a fixed scope
Chat and in-app assistants limited to approved answers on fees, limits and payments. Disputes, complaints and signs of vulnerability go straight to a person.
Chatbots and assistants
Case preparation agents
Agents that gather account history, screening hits and previous contact into one case file, then stop. The analyst reviews the file and makes the decision.
AI agents
How we work
From historical cases to a monitored model.
Our standard seven stages, with the work a financial firm needs inside them: labelled historical cases, a model record your risk team can review, and a person on every decision that affects a customer.
- 01
Discovery
We pick one queue — fraud alerts, onboarding reviews, service contacts — and measure it: volumes, handling time, false-positive rate and how outcomes are labelled today.
A baseline from your own queue
- 02
Strategy
We agree which technique fits, which data it may use and which decisions stay with people. Your risk and compliance teams see the plan before any build starts.
Agreed scope and decision boundary
- 03
UX & architecture
Analyst screens that show the reasons behind each score or extracted field, override controls, and the data flows, retention and logging underneath.
Review screens and a data-flow map
- 04
Development
Models trained and prompts written against a held-out set of your historical cases, with every change re-tested before it merges.
A pilot running on your own data
- 05
Testing
Accuracy and false-positive rates by segment, checks for unfair differences between customer groups, and tests of what happens when a provider or model is unavailable.
An evaluation pack for your risk team
- 06
Launch
Shadow mode first — the model scores live cases while analysts work as normal — then a staged switch-on with thresholds your team controls.
A controlled release with thresholds
- 07
Optimisation & support
Drift and performance monitoring, retraining on newly labelled outcomes, and threshold reviews with your team as fraud patterns and products change.
Monitoring and a retraining plan
Technology
The stack behind finance AI.
Chosen per job for accuracy, explainability, cost and where your data may go — including UK cloud regions and open models in your own account.
Scoring models
XGBoost and LightGBMIsolation forests and autoencodersscikit-learnSHAP for reason codes
Document AI
OCR and layout parsingVision-capable LLMsMRZ and field validationConfidence scoring
Language models
OpenAI GPT modelsAnthropic ClaudeGoogle GeminiLlama and Mistral (self-hosted)
Retrieval
EmbeddingsVector and hybrid searchPermission-aware indexesCited answers
Data and pipelines
PythonPostgreSQLFeature storesEvent streams and queues
Controls
Evaluation suitesDrift monitoringPII redactionAppend-only audit logs
We are not tied to a model vendor. Model and API fees are paid to the provider and sit outside our rates; we estimate them before the build.
Use cases
Finance jobs AI can prepare.
Bounded tasks with a clear owner. In each one, the output goes to a person who decides.
Company pack reading
Read incorporation certificates, shareholder registers and accounts, compare them with Companies House data and list discrepancies for the KYB analyst.
Scam payment warnings
Score outgoing payments for signs of a scam — a new payee, an unusual amount, a rushed sequence — so staff can step in with a warning or a call before money leaves.
Screening hit summaries
Lay out why a sanctions or PEP screening hit fired, with the matching and differing details side by side, for the analyst to clear or escalate.
Call and chat summaries
Turn customer calls and chats into case notes, flag possible financial difficulty or vulnerability, and suggest next steps for the agent to confirm.
Payslip and statement reading
Extract income, regular outgoings and employer details from uploaded payslips and statements, with each figure linked to its source for the underwriter.
Communications review
Check sampled calls, chats and emails against your conduct checklist and surface only the exceptions a compliance reviewer needs to read.
Why Techsleight
What working with us is actually like.
No inflated numbers — just how we run projects, and what you can hold us to.
Product engineering, not ticket-taking
We ask what the software is for before we estimate it — and we will tell you when something should not be built, or should be bought instead.
AI where it earns its place
LLM features, retrieval and automation built with evaluation, guardrails and cost controls, and plain software where that is the better answer.
Full-stack under one roof
Design, frontend, backend, mobile, cloud and QA in one team, so nothing falls between suppliers.
UK-focused delivery
UK business hours, estimates in pounds, and a contract with a UK company. Our engineers are based in the UK and India.
Flexible engagement
A fixed-scope project, dedicated developers or a monthly retainer — and you can move between them as the work changes.
You own everything
Code, IP, cloud accounts and documentation are yours from day one. We sign an NDA before discovery if you need one.
Support after launch
We stay on for fixes, upgrades and new features, or hand over cleanly to your in-house team with the documentation to match.
FAQs
Questions we get asked.
Straight answers on scope, cost, timelines and how we work. If yours is not here, ask us directly.
Which AI techniques suit fraud detection?
Usually not a large language model. Fraud scoring is usually better served by machine learning on structured signals — amounts, payees, devices, timings — using gradient-boosted trees or anomaly detection, combined with the rules your team already trusts. Language models help around the edges: summarising the case and drafting the note. We choose per job and explain why.
How do you explain a model’s score to an analyst?
Each score is stored with its inputs and the factors that pushed it up or down, calculated with methods such as SHAP and shown in plain words on the review screen. That gives the analyst something to check, and gives your firm material to explain an outcome. Whether an explanation is adequate for a customer is for your compliance team to judge.
We have very few confirmed fraud cases. Can we still train a model?
Often, yes, with the right approach: anomaly detection that learns normal behaviour, careful sampling, and labels collected from analyst outcomes as the system runs. We test on a later time period rather than random rows, so results reflect how the model will behave on future payments. If the data genuinely is not enough, we will say so in discovery.
Can AI approve or decline credit applications?
We do not build it to. AI can read documents, extract income and outgoings and prepare the file, but the lending decision follows your credit policy and your underwriters. Solely automated decisions with significant effects on people are restricted under UK data protection law, and your DPO and compliance team decide what safeguards any automation needs.
How do you test for unfair outcomes between customer groups?
We measure error rates and outcomes by segment — age band, region, product and other characteristics your firm is permitted to analyse — and look for proxies, such as postcode, that can stand in for protected characteristics. Differences are reported to your team with options to address them. Judging what is fair for your customers stays with your firm.
Will engineers outside the UK see our customer data?
Techsleight Labs is based in London, with onshore and offshore engineers in the UK and India. Most model work can be done on redacted, tokenised or synthetic data, and any access to production data is limited to named people, time-boxed and logged. If your policies require UK-only access to certain data, tell us at the start and we will state plainly what we can and cannot provide.
Where does our data go when we use a hosted language model?
To the provider you choose, under its business API terms, which we review with you during design — including data retention and whether a UK or European processing region is available. Personal data the model does not need is removed first. Where data must not leave your environment, we run open models in your own cloud account.
How do we know the model still works months after launch?
Monitoring tracks score distributions, alert volumes, analyst override rates and confirmed outcomes against the launch baseline. When the numbers drift, your team is alerted and we retrain or retune with the newly labelled cases. You see the same dashboard we do.
How much does an AI pilot for a financial firm cost?
A discovery sprint to pick the queue, check the data and agree the decision boundary starts from £2,000. The pilot is then quoted as a fixed-price project. Model and API usage is paid to the provider and sits outside our rates; we estimate it from your volumes before you commit.
Start a project
Have an alert, onboarding or service queue in mind?
Tell us which queue, roughly how many cases it handles and how outcomes are recorded today. We will tell you which technique fits, what data it needs and where a person should stay in the loop.
What happens next
- 1A senior engineer reads your brief within one working day, and replies with questions or a first view.
- 2A 30-minute call to understand the goal, constraints and what good looks like — no sales script.
- 3A written proposal with scope, milestones, team and a GBP estimate you can take to your board.
Techsleight Labs is a trading name of Krapton IT Consultancy.
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