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FIN · Fintech

Agents that assemble the evidence behind every compliance and credit decision

Banks, payment firms, lenders and insurers spend most of their risk and compliance hours gathering evidence and writing it up. FreeSeek builds agents that pull from core banking, AML engines, bureaus and filings, then hand a complete, cited file to the officer who decides. Every step is logged for model risk review and audit.

Systems we connect to
Core bankingNICE ActimizeCase managementData warehouseCredit bureau APIsBloombergWind
Application scenarios Case studies
Industry context

Where financial risk and compliance work stands today

Financial crime and credit teams have grown faster than revenue for a decade. Rules-based transaction monitoring still produces alert queues where the large majority close as false positives, yet each one needs a documented rationale. Onboarding a corporate client can take weeks because analysts trace ownership through registries in several countries and screen every related party by hand.

Regulators now expect the same rigour with less tolerance for backlog. Supervisors review the quality of case narratives, the timeliness of suspicious activity reports and the evidence behind credit decisions. At the same time, the EU AI Act, SR 11-7 and data protection laws require any model in these workflows to be validated, explainable and traceable. Automation has to be auditable from the first step.

Alert volumes outpace headcount

Monitoring scenarios tuned for coverage generate far more alerts than investigators can clear on time. Backlogs become supervisory findings.

Ownership is harder to see

Layered corporate structures, nominee directors and cross-border holdings slow KYB reviews. Beneficial ownership registers differ in coverage and format by country.

Regulatory change keeps arriving

New AML rules, consumer duty requirements and reporting templates land every quarter. Mapping each change to internal policies and controls is mostly manual.

Models must be explainable

Any tool that influences a credit or compliance outcome falls under model risk management. Teams need validation evidence and clear reasons, not opaque scores.

Agent run · example

Watch an agent work a real task

Every step is a tool call against your own systems, logged and reviewable. The result goes to a named person for sign-off.

Agent run · examplerun_5d81f0
TaskReview alert 88213: 14 cash deposits below the reporting threshold in 9 days on a retail account
  1. actimize.alert()
    Loaded alert, customer profile and expected monthly activity of 3,000 EUR
  2. corebanking.txn()
    Found deposits at 6 branches followed by same-day transfers to a newly added beneficiary
  3. case.history()
    Linked 2 prior alerts on the same beneficiary, both closed without review of onward flows
Result · Recommended escalation with a draft STR narrative; sent to the investigator for decision and MLRO sign-off
Application scenarios

Four workflows, analysed step by step.

Each scenario shows how the work is done today, what the agent takes over, where a person signs off, and what we measure.

FIN-1KYC/KYB onboarding reviewFIN-2AML alert triage and SAR narrative draftingFIN-3SME credit memo draftingFIN-4Regulatory change impact analysis
FIN-1

KYC/KYB onboarding review

The agent builds a complete onboarding file with ownership chain, screening results and a risk rating proposal for the reviewing analyst.

Integration depth Moderate
Typical pilot6–8 weeks
How it works today

Analysts collect incorporation documents, search company registries in each jurisdiction, draw ownership charts by hand and run every director and shareholder through sanctions, PEP and adverse-media screening. Media hits are mostly name matches that take time to dismiss. A single multi-layer corporate client can take one to three weeks, and files often go back for missing evidence after quality review.

Systems used
Company registriesSanctions and PEP screeningAdverse-media searchCase managementCore banking
What the agent does
  1. Extracts entities, officers and shareholdings from submitted documents and registry records, and builds an ownership tree down to the 25% threshold or the bank's lower policy threshold
  2. Screens each natural and legal person against sanctions and PEP lists and runs adverse-media searches in the relevant languages
  3. Clusters screening hits, discards clear false matches with a stated reason and flags hits that need analyst judgement
  4. Checks the file against the bank's CDD and EDD checklist and lists missing documents to request from the client
  5. Drafts the risk rating rationale with citations to every source used
Human checkpoint
A KYC analyst confirms the ownership tree and decides on every unresolved screening hit. Relationships rated high risk go to a senior compliance officer for approval under the bank's EDD policy.
What we measure
  • Onboarding cycle time
  • Files returned by quality assurance
  • Screening hits auto-dispositioned with stated reason
  • Requests for information per client
FIN-2

AML alert triage and SAR narrative drafting

The agent investigates transaction-monitoring alerts, recommends a disposition and drafts the SAR or STR narrative for the investigator.

Integration depth Deep integration
Typical pilot8–12 weeks
How it works today

Level-one investigators open each alert, pull twelve months of transactions, look up counterparties, check prior cases and write a closure note. Most alerts close as false positives, but each closure needs a documented rationale. When activity is suspicious, the investigator writes the narrative from scratch under a filing deadline, and narrative quality varies widely between staff.

Systems used
NICE ActimizeCore bankingCase managementData warehousegoAML
What the agent does
  1. Pulls the alert, customer profile, expected activity and transaction history from the monitoring engine and core banking
  2. Analyses flows for typologies such as structuring, rapid movement of funds and unusual counterparties, and links related alerts and prior cases
  3. Proposes a disposition with the specific facts that support it
  4. For escalations, drafts a SAR or STR narrative in the who, what, when, where and why structure expected by the financial intelligence unit
  5. Writes the full evidence trail and reasoning to the case management record
Human checkpoint
An investigator accepts or overrides every disposition. The MLRO or a delegated officer makes the decision to file and signs off the final narrative.
What we measure
  • Alert handling time
  • Alert backlog age
  • Investigator override rate
  • Narrative quality score from QA review
FIN-3

SME credit memo drafting

The agent spreads financial statements and bank statements and drafts a credit memo for the relationship manager and credit officer.

Integration depth Moderate
Typical pilot6–8 weeks
How it works today

Relationship managers or credit analysts re-key financial statements into spreading templates, reconcile them with bank statements and bureau reports, and write the memo narrative. Small business files arrive as scanned PDFs, management accounts and statements in varied formats. Preparing one memo often takes most of a working day, which limits how many small facilities a team can underwrite economically.

Systems used
Credit bureau APIsCore bankingLoan origination systemData warehouse
What the agent does
  1. Extracts and spreads income statement, balance sheet and cash flow items into the bank's template
  2. Categorises bank statement transactions to estimate recurring revenue, cash burn and existing debt service
  3. Pulls bureau data and checks covenant and policy criteria such as debt service coverage and leverage limits
  4. Drafts the credit memo with business overview, financial analysis, key risks, mitigants and a list of policy exceptions
  5. Flags inconsistencies between declared figures and observed bank flows
Human checkpoint
The credit analyst verifies the spreading and edits the memo. The credit decision, pricing and any policy exception stay with the authorised credit officer or committee.
What we measure
  • Memo preparation time
  • Spreading corrections per file
  • Application-to-decision time
  • Policy exceptions identified before committee
FIN-4

Regulatory change impact analysis

The agent tracks new rules and guidance, maps each obligation to internal policies and controls, and drafts a gap assessment.

Integration depth Quick win
Typical pilot4–6 weeks
How it works today

Compliance teams read regulator publications, consultation papers and supervisory letters, then search policy libraries to find what is affected. Mapping is kept in spreadsheets that fall out of date. Large changes, such as a new AML regulation or reporting template, take weeks of workshops before owners know what they must change and by when.

Systems used
Regulator websites and feedsPolicy management systemGRC platformBloomberg
What the agent does
  1. Monitors regulator and standard-setter publications and extracts individual obligations with effective dates
  2. Matches each obligation to existing policies, procedures, controls and reports in the bank's library
  3. Classifies each match as covered, partially covered or a gap, quoting the relevant clauses side by side
  4. Drafts the impact assessment with proposed owners and deadlines for the compliance team
Human checkpoint
Compliance officers confirm each gap classification and assign owners. Policy changes go through the bank's normal approval and governance committees.
What we measure
  • Time from publication to impact assessment
  • Obligations mapped to controls
  • Gaps closed before effective date
  • Reviewer changes to proposed mappings
Case studies

How engagements in this field have played out.

Illustrative cases: composites of typical deployments with client details removed. Figures show the range our pilots are designed to reach.

Clearing a transaction-monitoring backlog without lowering standards

Client: Mid-sized retail and commercial bank, 2.4 million customersRegion: Southern Europe
−41%average alert handling time
−63%backlog age after 5 months
+28%narrative QA score
Challenge

The bank carried an alert backlog of over four months after a supervisory review asked it to widen monitoring scenarios. Investigators spent most of their time collecting data rather than analysing it. Closure rationales were inconsistent, and the regulator had flagged weak narratives in a sample of filed STRs.

What we built
  • Connected the agent to the monitoring engine, core banking and case management in read-only mode
  • Built disposition proposals with cited facts for level-one alerts and drafted STR narratives for escalations
  • Ran a parallel period in which investigators worked alerts with and without the agent and compared outcomes
  • Documented the agent for model risk validation, including override tracking and periodic sample testing
Timeline: 10-week pilot, production in month 5

Faster corporate onboarding for a payments provider

Client: Licensed payment institution serving cross-border e-commerce merchantsRegion: GCC
−35%onboarding cycle time
−30%files returned by QA
−22%information requests per merchant
Challenge

Merchant onboarding took an average of 12 working days because many applicants had holding companies in several free zones and offshore jurisdictions. Analysts traced ownership manually and cleared large volumes of adverse-media name matches. Sales teams lost merchants to faster competitors.

What we built
  • Built an ownership extraction and tree-building step across the registries used most often
  • Added multilingual adverse-media screening in Arabic and English with hit clustering
  • Generated missing-document requests automatically from the EDD checklist
Timeline: 8-week pilot, production in month 4
Risk and compliance

Model risk, compliance and data controls

Model risk management

Each agent is inventoried and documented as a model or tool under SR 11-7 and equivalent supervisory expectations. Validation covers conceptual soundness, outcome testing against analyst decisions and ongoing monitoring of override rates.

AML standards and filing duties

Workflows follow the FATF Recommendations on customer due diligence, beneficial ownership and suspicious transaction reporting, as implemented in local law. The agent prepares the evidence and narrative, and the decision to file stays with the MLRO or delegated officer.

EU AI Act and automated decisions

Creditworthiness assessment of natural persons is a high-risk use under the EU AI Act, which requires risk management, human oversight and logging. Under GDPR Article 22, the agent does not make solely automated decisions with legal or similarly significant effects on customers.

Data protection and audit trail

Deployments respect GDPR, China PIPL and local banking secrecy rules, with data kept in the client's environment or approved regions. Every query, source and draft is logged so auditors can reconstruct how a recommendation was produced.

Questions we hear
Does the agent decide whether to file a SAR or approve a loan?

No. The agent gathers evidence, proposes a disposition and drafts the narrative or memo. An authorised investigator, MLRO or credit officer makes and signs every decision, and overrides are recorded.

How does this fit with our model risk management framework?

We provide model documentation, test results against historical decisions and monitoring metrics in the format your validation team uses. Most clients register the agent in the model inventory before the pilot moves to production.

Which systems do you need access to?

Typically read access to the monitoring engine, core banking, case management and the data warehouse, plus screening and bureau APIs. Write access is limited to creating drafts and notes in case management.

Where is our customer data processed?

Inside your environment or in a cloud region you approve. We align the deployment with GDPR, PIPL, banking secrecy rules and your outsourcing policy, and no customer data is used to train shared models.

Have a workflow like these?

Send us a short description of how the work is done today. We reply within two business days.

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