AI by industry
AI for fintech and banking you can audit
Krapton builds AI for banks, lenders, payment companies and fintech startups: onboarding document checks, fraud and AML alert triage, underwriting support and service assistants. Every output carries its evidence, and your people make the decisions that affect customers.
01 /
Where AI earns its place in financial services
- OpenAI
- Claude
- LangGraph
- Python
- FastAPI
- scikit-learn
- PostgreSQL
- Apache Kafka
- Snowflake
- Next.js
- Terraform
Much of the work inside a bank or fintech is reading and checking: identity documents against the application, bank statements against stated income, alerts against transaction history, complaints against policy. It is slow and repetitive, and it is also where one missed detail becomes an audit finding. We build AI that does the reading, pulls out what matters and hands the analyst a prepared case, so their time goes into judgment instead of retyping.
In finance, the question is rarely whether a model can do the task. It is whether you can defend the result to an examiner, an auditor or a customer. So we design around evidence: each extracted field points back to the page it came from, each risk score arrives with the reasons behind it, and each action lands in an audit log your compliance team can query. Decisions that affect customers stay with your staff and your approved rules.
02 /
What we build for banks, lenders and fintechs
Each use case starts from a workflow your team already runs and adds AI where reading, matching or drafting slows it down.
KYC and onboarding documents
Read identity documents, proof of address and company registration papers, cross-check them against the application and sanctions screening results, and send mismatches or unreadable fields to an analyst instead of rejecting the applicant.
Fraud and AML alert triage
Gather the transactions, linked accounts and earlier cases behind each alert, rank the queue by risk and draft the case narrative, so investigators begin with context. Every disposition is still made by a person.
Underwriting support for lenders
Read bank statements, pay stubs and tax returns, calculate income and flag inconsistencies for the underwriter. Credit decisions stay with your approved models and policy, with the reasons recorded for adverse action notices.
Customer service assistants
Answer account, card and payment questions from your approved help content, pass the conversation to an agent with a summary when a person is needed, and recognize complaints and signs of financial hardship early.
Reconciliation exceptions
Work through the payments, invoices and settlement records your matching rules could not pair, propose the most likely match with its reasoning, and leave the posting decision to an operations analyst.
Regulatory change and policy search
Track new rules and guidance, map each change to the policies and controls it affects, and answer staff questions from your policy library with a citation to the exact paragraph.
03 /
What you receive, ready for risk and compliance review
A working system in your environment
The application, integrations and infrastructure code, deployed in your cloud account so customer data stays inside your own security boundary.
Model documentation
A plain-language record of each model's purpose, data sources, limitations and controls, written to support your model risk management and validation process.
Evaluation and fairness report
Accuracy measured on cases your team has already decided, errors broken down by segment, and fairness testing wherever an output could influence access to credit or services.
Reviewer console and audit log
The screen analysts use to accept, correct or escalate each suggestion, and a searchable record of every input, output, reviewer and override.
Monitoring and runbook
Dashboards for drift, error rates and model cost, agreed alert thresholds, and written steps for your team to follow when a number moves.
Training and handover
Working sessions for analysts, reviewers and engineers on operating the system, reading its evidence and changing prompts or thresholds safely.
04 /
How a fintech AI project runs
Each stage produces something your risk and compliance colleagues can sign off before work moves on.
01
Map the workflow and its rules
We sit with operations and compliance to trace the process, find where the time goes, and agree which steps AI may assist, which need human sign-off, and which obligations apply, from KYC and fair lending to PCI DSS.
02
Design the controls first
Data access, retention, masking of card and account numbers, audit logging and reviewer checkpoints are designed before a model is chosen, so the controls shape the system rather than being fitted around it later.
03
Test on your own history
We build an evaluation set from files your team has already decided, measure accuracy and failure patterns against it, and check for disparate outcomes across groups wherever the output could sway a customer decision.
04
Shadow first, then go live
The system first runs alongside your team without touching customers, so its suggestions can be compared with real decisions. It joins the live workflow once your risk owners accept the results, with monitoring in place before launch.
05 /
Regulation, fairness and accountability
Rules written before large language models existed still apply to them. Model risk guidance expects validation and ongoing monitoring. Fair lending law requires specific reasons when credit is denied, however complex the model behind the decision. KYC and AML programs need a documented basis for each alert that is closed. PCI DSS covers any component that handles card data, GLBA and GDPR govern customer information, and the EU AI Act classes credit scoring of individuals as high risk.
Your compliance team decides how these obligations apply to your business. We make their answers easy to evidence: human review wherever an output affects a customer, reasons and sources stored with every decision, data minimization so each model receives just the fields its task requires, and model access on business terms that keep your data out of training. If the system falls inside your SOC 2 scope, we document its controls for your auditor. The assistants we build explain your products and policies; they are not designed to give investment, tax or credit advice.
07 /
Related services
Finance software
We build wealth-tech, treasury, capital-markets and CFO platforms where every decimal, every reconciliation and every audit trail matters.
Banking software
We engineer digital banking, lending and account-opening platforms with the security, observability and regulatory rigour modern banks expect.
Lending software
We build LOS, LMS and digital lending stacks — from instant-personal-loan apps to SME, BNPL and supply-chain finance platforms.
Create customer support AI
You need an AI layer over your support stack — triage, summarise, draft replies — without ripping out tooling.
Build LLM evaluation pipeline
You ship LLM features by vibes — there's no automated eval, so model swaps and prompt changes are pure gut feel.
AI for insurance
Claims intake, underwriting support and policy answers.
08 /
Frequently asked questions
How is AI used in banking today?
Mostly in the middle and back office: reading onboarding documents, triaging fraud and AML alerts, supporting underwriters, reconciling exceptions and answering customer questions from approved content. The strongest uses keep a person responsible for the decision and give them better-prepared information. Fully automated customer decisions are possible in narrow cases, but they carry the heaviest regulatory and fairness obligations.
Can lenders use AI without breaking fair lending rules?
Yes, when the design supports it. Lenders must give specific reasons for adverse action, avoid discriminatory outcomes and be able to explain their models to examiners. We keep final credit decisions within your approved policy, record the factors behind each recommendation, and test outcomes for disparities across groups before launch and after it. Your compliance and legal teams approve the approach.
Will our customer data be used to train AI models?
No. We use enterprise endpoints from providers such as OpenAI, Anthropic, Microsoft Azure and Google Cloud, whose business terms exclude training on your data, or open models hosted in your own cloud when data must stay there. Card and account numbers are masked or tokenized before a model sees them, and retention follows your policy.
Can AI reduce false positives in AML monitoring?
It can help analysts clear them faster, which is where much of the cost sits. We leave your monitoring rules in place and add a layer that gathers the context behind each alert, ranks the queue and drafts the disposition narrative. Analysts still decide every alert, and any change to thresholds goes through your model governance.
Can you connect AI to our core banking system?
In most cases, yes, through the APIs, message queues or file exchanges your core, loan origination and card platforms already offer. Where a legacy system only produces batch files, we work from those. We confirm what each system allows during discovery, before any estimate is final, and read-only access is the default wherever the workflow permits it.
What does model risk management mean for an AI project?
Banks are expected to validate their models, understand where they are weak and keep watching them in production, and supervisors apply that expectation to AI too. We make validation practical by documenting each model's purpose, data and known limits, keeping evaluation sets and results under version control, and giving your validators access to test the system independently.
Ready to build AI that actually works in production?
Tell us about your AI project and get a free technical consultation within 24 hours. We'll map your use case, assess your data, and give you an honest feasibility assessment — no sales pitch.