Industries

Financial Services

Payments, ledgers and reporting an auditor can follow.

Financial services does not have an AI adoption problem; it has a model governance problem. Every automation has to be explainable to a regulator, reversible, and defensible line by line. We build with that constraint first — which is also why the systems end up faster, because you cannot govern what you cannot trace.

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Google Cloud for financial services

We build on the pieces this stack is strongest in: AML AI for financial-crime detection, BigQuery and the risk-data platform, Apigee for embedded finance and consent-aware APIs, and Gemini for document-heavy operations — all inside a model governance framework your risk function signs off.

01 · Collect

Apigee X · Core events

Consent-aware APIs over the core you are not replacing yet.
02 · Score

AML AI · BigQuery

Explainable risk scores, per alert, per case.
03 · Investigate

Gemini document processing

Case files summarised; every decision logged and reversible.
04 · Report

Risk data platform · Regulatory reporting

The disclosure becomes a query, not a quarterly exercise.

3.4×: more confirmed suspicious activity detected in the financial-crime programme we benchmarked against; Source: Client engagement, anonymised
58.7%: reduction in analyst alert volume on the same programme; Source: Client engagement, anonymised

SASB FN-CB — Commercial Banks, with FN-IN for insurance

SASB standards ↗

For a bank, most emissions sit in the loan book, not the offices. SASB’s finance metrics are mostly social and governance — which is why data security and credit-analysis integration lead, with financed emissions handled as an extension.

FN-CB-410a.1
ESG in credit analysis

% of credit exposure assessed against environmental and social criteria

ESG factors attached to the credit record, so the percentage is a query over the portfolio rather than a sample.
FN-CB-230a.1
Data security

breaches, records affected, % involving PII

Field-level classification across core and analytics stores, with access events retained for the regulator.
FN-CB-240a.1
Financial inclusion

participants in inclusion products, by product type

Product tagging in the core plus eligibility logic, so the disclosure is derived from actual accounts.
FN-IN-410a.1
Insurance: climate exposure

premiums written with environmental/social benefit; physical risk to insured assets

Geospatial risk factors joined to the policy record — the same join that improves underwriting.

Sustainability disclosures, their SASB code, the metric and how we would instrument it
Standard	KPI	Metric	How we instrument it
`FN-CB-410a.1`	ESG in credit analysis	% of credit exposure assessed against environmental and social criteria	ESG factors attached to the credit record, so the percentage is a query over the portfolio rather than a sample.
`FN-CB-230a.1`	Data security	breaches, records affected, % involving PII	Field-level classification across core and analytics stores, with access events retained for the regulator.
`FN-CB-240a.1`	Financial inclusion	participants in inclusion products, by product type	Product tagging in the core plus eligibility logic, so the disclosure is derived from actual accounts.
`FN-IN-410a.1`	Insurance: climate exposure	premiums written with environmental/social benefit; physical risk to insured assets	Geospatial risk factors joined to the policy record — the same join that improves underwriting.

Where we help

Core and ledger modernisation: The parts of the core you can move without a two-year freeze: event-sourced ledgers, API façade over the incumbent, reconciliation that proves the cutover.
Risk and AML operations: Alert triage with explainable risk scores, false-positive reduction, and a full decision trail per case. Automation assists the investigator; it does not replace the judgement.
Distribution and service: Onboarding, KYC refresh and servicing journeys that keep a human in the loop where regulation requires one, and remove them where it does not.

Working on something in Financial Services?

A 30-minute call is usually enough to work out whether this is a quick win or a rebuild — and we will tell you if it is the second one before you spend anything.

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How much analyst time goes to false positives?

Alert triage is the most measurable automation in financial crime. Model the hours currently spent on alerts that go nowhere.

Indicative

Financial Services

Recoverable / year

$6.6M

Alerts per year: 300k
Hours on false positives / year: 161k
Cost of false positives / year: $11M

Alerts per month 25 k

1 k 500 k

False positive rate 92%

50% 98%

Minutes per alert review 35 min

5 min 180 min

Fully loaded analyst cost / hour 70 $

Model assumptions

Applies a 58.7% false-positive reduction — the median we have seen on anonymised financial-crime programmes.
Assumes triage effort is uniform across alerts; the detailed model weights by alert type.
Excludes model governance, validation and audit cost, which are real and must be budgeted.

An order-of-magnitude model, not a quote. We will send the version with your real baselines, the assumptions replaced, and the implementation cost netted out.

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