AI readiness for financial services companiesRegulated firms reconcile every day. Just not across every system.
Financial firms keep accurate ledgers because regulators insist. The trouble is the joins: the client in the CRM, the account on the core platform, the complaint in support and the payment in finance rarely share one ID. That is where fraud, onboarding and service AI stalls.
Starts with a 30-minute call. Commits you to nothing.
The AI on the table
The AI plays proposed in financial services.
What vendors and plans put forward in financial services, what each play needs from the data, and the gap that holds it back. In a real Read, each call rests on your Truth Score™ (a score out of 100 for how far your data can be trusted) and the records behind it. The calls here are an illustrative read, not a benchmark.
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Payment and transaction fraud detection
“Flag suspicious payments as they happen”
What fraud detection needsBlocked- Needs
- Consistent counterparties and recorded past cases
- Held back by
- Duplicate clients across the platform and CRM
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Onboarding document automation
“Read onboarding and identity documents automatically”
What document intelligence needsNeeds work- Needs
- Extracted details that match the client record
- Held back by
- Several versions of the same client file
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Client retention and attrition
“Spot clients likely to lapse or move assets”
What customer churn prediction needsNeeds work- Needs
- One client across CRM, platform and complaints
- Held back by
- Complaints tied to no client record
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Complaints triage and service agent
“Triage complaints and answer routine queries”
What customer service automation needsNeeds work- Needs
- Cases tied to clients and products
- Held back by
- Products named differently in support and on the platform
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Close and reporting automation
“Automate reconciliations and management reporting”
What financial close and FP&A needsDeploy now- Needs
- Platform balances that tie to the ledger
- Watch for
- Manual journals with no owner
Illustrative example, not a client. Your calls depend on your records.
The systems estate
The systems behind the plays.
Written for specialist lenders, wealth and asset managers, insurance brokers and payments firms in the mid-market, running a core platform beside a CRM, a finance system and a good deal of spreadsheet work. Each system is built to be right about one thing. None was built to agree with the others, and the plays above all run across more than one.
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Core platform
Loans, policies or portfolios
Loan, policy or portfolio administration
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CRM
Clients, advisers, introducers
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Finance
Ledger, fees, commissions
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Complaints and support
Cases, complaints, outcomes
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HR and payroll
Staff, approvers, signatories
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Data warehouse
Regulatory and management reporting
Typical, not exhaustive. Anything that exports to CSV or Excel can be included.
The gaps
Every system looks clean on its own. The truth is in the gaps between them.
One record traced across your core platform, the CRM and the ledger: each system right about its own part, and the three still disagree. Then the gaps that hold the plays back. Which of them exist in your data is what a Truth Score Read finds out.
Where the plays sink
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Platform ↔ CRM
Clients on the platform with no CRM record, and the reverse
SinksRetention models and service agents
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Platform ↔ Finance
Balances and fees on the platform that don’t tie to the ledger
SinksClose automation and fraud baselines
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Support ↔ Platform
Complaints logged against no product or account
SinksComplaints analytics and conduct reporting
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HR ↔ Finance
Payment approvers and signatories who have left
SinksFraud controls
Gaps to test in the sector, not client findings.
Regulation
The rules that shape which plays you can run.
Kept short and honest. This is not legal advice: we evidence what your data can support; your counsel decides what the rules require.
The EU AI Act
AI used to assess the creditworthiness of individuals, or to price life and health insurance for them, is listed as high-risk, with duties on data governance and record keeping.
GDPR and automated decisions
Automated decisions with significant effects on individuals carry specific rights, including human review. Clean, explainable inputs are part of meeting them.
Operational resilience (DORA, in the EU)
ICT risk rules for financial entities reach third-party technology, AI vendors included. A record of what the data was and how it was checked supports that file.
Your supervisor
Supervisors expect model risk management: knowing what a model learnt from and how it was validated. We evidence the data side; we are not your model validator.
Your plan, tested
Got a plan that names these plays?
Send it with your exports. A Truth Score Read tests the AI in it against your own records, within 5 working days of complete exports. Go further with an AI Investment Review: every bet ranked, your plan tested play by play, and a roadmap to hand to your own team.
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Send the plan, if there is one
The business plan, growth strategy, value creation plan or the vendor proposal. We pull out the AI plays in it and the targets that lean on them.
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Send exports from two or more systems
Usually core platform and CRM to start. Spreadsheets count. Under NDA, using the field list we send.
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Test each play against the records
Each play mapped to the data it needs. Our engine matches the customers, people, deals and invoices across your exports to see whether that data is there and agrees, and every red flag is checked by hand against sample records.
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Get the verdict, play by play
A signed memo: your Truth Score, a Leverability read marking each play Deploy now, Needs work or Blocked with the reason in your own records, and a verdict: sign, fix first or walk away.
What you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Not a software company? The sample is one, but the method doesn’t change; only the systems do. For a financial services business the same match runs across your core platform, the CRM and the ledger, and every red flag is checked by hand the same way.
Questions
AI in financial services, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callWe are regulated. Can you work with our data at all?
Yes, from exports agreed in advance: an NDA and a data processing agreement first, raw exports deleted after the engagement, and the full list of sub-processors with the data processing agreement, before any data moves. We keep quality metrics, not your records.
Does this replace model validation?
No. Model validation tests the model. We test the data it would learn from and act on, and whether the systems agree. The two fit together; neither replaces the other.
Which play is likely to be ready first?
Your records decide, not the sector. Plays that live inside the ledger start with an advantage, because regulators insist ledgers are well kept. Client-facing plays depend on one client ID agreeing across the platform, the CRM and support, which is a harder test.
Can our core platform be included?
Any system that exports can be included. Core platforms usually produce client, account and transaction reports; we send the field list after the call.
Before financial services AI gets funded, find out what your data can carry.
Exports from two or more systems, plus the plan or proposal if there is one. A signed verdict, play by play, within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.