IndustriesFinancial institutions & fintech

Your score is ready in seconds. The decision still takes a week.

The rating comes back at once; then someone opens four systems for the statements, recalculates exposure by hand, and works out who may sign. We build the models, small language models, and agents that prepare that file for the person who signs, inside your environment.

Start with a Decision BlueprintTalk to the engineer
FIG. 1 · Fraud alert case noteIllustrative
Fraud alertCard-not-present payment

Model · fraud score

Highscore band

Agent · parallel checks

  • DeviceNew device, first seen 2 hours beforedevice log
  • BeneficiaryMerchant linked to 3 confirmed-fraud casesnetwork
  • Customer patternNo prior purchase in this categoryhistory
  • Model
  • Agent

Why a person decidesRouted to an analyst: not a listed false-positive pattern.

DecidesFraud analyst

From the use case: Fraud alert investigation

Use cases in this sector

The desks we build for here.

Lending and credit risk

  • ModelsSLMsAgentic AI

    SME credit referrals

    TodayReferred files wait while analysts rebuild the firm from four systems and last year's committee notes.

    With the systemOne file with the rating, its reasons, and the authority level.

    Credit manager decides

  • Models

    Application scorecard for new-to-bank applicants

    TodayMost applicants are new to you, the bureau file is thin, and your only outcomes are for the people you approved.

    With the systemA credit scoring model beside today's rules: a scorecard, with the default probability by band.

    Credit policy committee decides

  • ModelsAgentic AI

    Behavioral score and limit management

    TodayLimits are set once at application; good customers outgrow them and quietly deteriorating ones keep full, undrawn lines.

    With the systemLimit actions queued with their evidence, inside the committee's matrix.

    Underwriter decides

  • ModelsSLMsAgentic AI

    Early warning on the loan book

    TodayThe borrower is current; other banks are already cutting its limits, and you see its quarter only at renewal.

    With the systemA file, not a spreadsheet: signals checked in parallel and gathered in one place.

    Watch-list committee decides

  • SLMs

    Corporate credit memo and financial statement analysis

    TodayAnalysts spread statements and write the memo by hand, in a fixed format, for every renewal.

    With the systemStatements spread with their sources, and the figures to check flagged.

    Credit analyst decides

  • Models

    Risk-based pricing with price response

    TodayYour price covers risk and capital, yet you lose good customers over a few basis points and discount those who would have signed anyway.

    With the systemA price grid that shows how each segment responds to a different rate.

    Pricing committee decides

  • ModelsSLMsAgentic AI

    Early arrears collections

    TodayCalls reach customers who would have paid anyway, while the ones who cannot keep up need a plan, not ten calls.

    With the systemHardship told apart from won't-pay, with the plan's arithmetic on file.

    Collector decides

Fraud and financial crime

  • ModelsAgentic AI

    Fraud alert investigation

    TodayMost alerts are false, and each one still takes an analyst real time to rule out.

    With the systemEach alert arrives with one note; sanctions hits stay with a person.

    Fraud analyst decides

Insurance

  • ModelsSLMsAgentic AI

    Insurance claims fast-track

    TodaySimple claims wait in the same queue as hard ones, and customers wait with them.

    With the systemSimple claims take the fast track through your own process; the system never declines a claim.

    Claims adjuster decides

Leasing, payments, and fintech

  • ModelsSLMsAgentic AI

    The lease file

    TodayA dealer sends a proforma invoice; you must know if the lessee is good, the price is right, and the machine is not already financed elsewhere.

    With the systemThe lessee, the price, and other financing checked in one file.

    Credit committee decides

  • ModelsSLMsAgentic AI

    Merchant onboarding and monitoring

    TodayNew merchants sign up daily; one merchant set up to defraud or launder can cost more than many good ones earn.

    With the systemEach new merchant checked against your policy before anyone signs. Nothing is automatic.

    Onboarding officer decides

Customer and operations

  • ModelsSLMs

    Next best offer

    TodayEvery customer gets the same campaign, and the relationship manager does not know what changed for this one.

    With the systemOne offer for this customer, with its reason; nothing is sent automatically.

    Relationship manager decides

  • SLMsAgentic AI

    Regulation to policy

    TodayA new circular arrives, and nobody knows which sections of your policies it changes.

    With the systemEach policy section marked changed or not, with the reason on record.

    Compliance officer decides

  • SLMs

    Request and complaint routing

    TodayThousands of messages a day land in one queue; misroutes miss legal deadlines.

    With the systemEach message routed to the desk that owns it; the model never closes one.

    Head of operations decides

These use cases describe systems we build, written from how these desks work. They are not past client projects: no client names, no results.

The sector

What is different here

The way of working stays the same. These four things change with the sector.

Data
Bureau and core-banking data, financial statements, collateral and policy documents, and transaction streams: much of it personal, all of it under banking secrecy.
Who signs
The person or committee your authority matrix names: credit, fraud, claims, compliance, or operations. Your own policy engine approves only inside the limits it already has; the agent never holds credit authority.
Where it runs
On your servers; banking data stays in Türkiye where the rules require it.
Rules you work under
  • Model governance: an intended use, documentation, and monitoring for every model
  • Independent validation before a model goes live: we prepare the evidence, your validation unit judges it
  • The Basel and International Financial Reporting Standard 9 (IFRS 9) model lifecycle: development, validation, monitoring, recalibration
  • Loan origination and monitoring under the European Banking Authority's guidelines (EBA/GL/2020/06): early-warning indicators, watch lists, and risk-based pricing
  • The Banking Regulation and Supervision Agency's (BDDK) information-systems rules and the special-category data rules of the Personal Data Protection Law (KVKK): banking data kept in Türkiye where the rules require it
  • Banking secrecy under the Banking Law on credit files and committee notes; suspicious-transaction reports to MASAK, the financial intelligence unit, are always a person's

Where it runs, what it connects to, and who can reach it

Kinds of AI

The three kinds of AI here

Each works on its own; they stack where the decision needs more.

  • Estimate

    Machine-learning models

    Scorecards, early-warning, pricing, and fraud models your validation unit can read, challenge, and monitor.

    How it works

  • Understand

    Small language models (SLMs)

    A small language model trained on your credit files and policies reads statements and drafts the memo in your format, citing each source.

    How it works

  • Prepare and act

    Agentic AI systems

    Agents gather the evidence and prepare each file, acting only inside the limits your policy writes; everything else goes to a named person.

    How it works

Equalize the gap.

Bring one decision. Leave knowing the first step.

A short description of the decision is enough; no confidential records.

Start with a Decision BlueprintTalk to the engineer

https://eqlx.ai/industries/financial-services · Printed copy; the website is the current version.