Financial services AI consulting for banks, fintechs and insurers
AI consulting for fintech companies and banks starts at USD 80,000 with Paul Okhrem. AI in financial services consulting connects a business case to data, vendors, tests and accountable owners. Paul helps banks, fintechs and insurers decide what to fund, how to control it and when to stop.
Price: USD 1,000 per hour, 80-hour minimum, USD 80,000 floor. Delivered personally by Paul Okhrem. See the published consulting terms. Engineering, software and external specialists cost extra unless included in writing.
AI consulting for financial services: what it covers
Paul Okhrem connects AI strategy, controls and delivery decisions for financial services leaders. The work defines one decision, its owner and the evidence needed to approve it.
- Bank or fintech pilot: move one use case toward production with a clear model-risk review and release owner.
- Insurance expansion: set AI controls before scaling a claims or underwriting pilot.
- Fintech board review: explain AI controls, costs and open risks to an investor, bank partner or regulator.
- Not a fit: licensed regulatory advice, legal opinions, credit approval or an outsourced compliance function.
If the design and approvals are complete, an engineering team may be the right next hire. This engagement fits an unresolved leadership decision, not a request for delivery capacity alone.
AI decisions banks face
Choose the workflow and acceptance test before choosing a model. Each pilot needs a baseline, an accountable owner and a way to stop unsafe actions.
| Workflow | Measure before the pilot | Control to test |
|---|---|---|
| Staff policy assistant | Time to find the correct policy | Source citations, access permissions and refusal when evidence is missing |
| Document review support | Review time and missed exceptions | Human sign-off and traceable source passages |
| Customer service triage | Correct routing and repeat contacts | Escalation before advice, account changes or sensitive disclosures |
| Operations exception queue | Time to resolve and rework | Action limits, audit trail and rollback |
These are possible consulting scopes, not named bank outcomes. A working staff assistant does not validate a lending, trading or customer-eligibility use case.
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Agentic AI in financial services
Agents fit work with several steps and clear evidence, such as KYC file preparation or reconciliation exceptions. Paul helps define what an agent may prepare, what it may change and who approves the result.
- Know your customer (KYC): collect file evidence and flag missing items for an authorized reviewer.
- Alert triage: gather facts and record the reasoning before a specialist reviews the case.
- Disputes and credit memos: draft from approved records, without granting financial or customer authority.
When an action affects a customer, capital or a regulator, the agent prepares and a person decides. Put each agent in the model inventory with a named owner, tests and monitoring.
Read the AI in banking guide for use cases and a production checklist.
Fintech AI consulting
AI consulting for fintech companies covers feature choices, vendor checks and evidence for bank-partner review. Paul advises fintech CEOs and product leaders on what to build, how to test it and how to control customer-facing AI.
A fintech AI consultant should connect the product roadmap to the cost and risk of each feature. Compare a custom build, a vendor product and a fixed workflow. Check tenant data access, model usage costs and the effort needed to switch providers.
- Define which AI features support the product's value rather than a demonstration.
- Document model inputs, tests, known errors and change controls for bank partners.
- Set human review before advice, financial actions or sensitive disclosures.
- Plan a fallback when a vendor fails or changes its model.
Prepare the evidence before asking a bank partner to approve the use. Paul can challenge gaps and organize the review; he cannot promise acceptance. Model risk work follows AI governance consulting.
AI consulting for banks
Banks hire an AI banking consultant when one decision spans risk, technology and operations. Paul connects use-case choice, model-risk review, agent controls and board reporting within a scoped engagement.
Bank AI consultants should identify who approves data use, vendor access, model changes and release. A bank may need several approval owners and legacy-system checks. A faster fintech pilot still needs tests for data separation, transaction limits and customer recourse.
The output is a decision record with evidence, gaps and owners. It is not a large staffed program or a replacement for the bank's control functions.
AI for insurers
Paul Okhrem provides insurance AI consulting for carriers and insurtechs reviewing claims, underwriting support and policy service. The scope sets useful tests and human controls. Qualified insurance, actuarial and legal specialists retain their approvals.
Match each insurance workflow to its risk
A staff knowledge tool and a system affecting a premium have different consequences. Start with the insurance line, intended decision, customer journey and markets served.
| Scenario | Useful evaluation | Decision boundary |
|---|---|---|
| Claims document support | Missing facts, review time and correction rate | Qualified claims staff retain settlement and coverage decisions |
| Underwriting research support | Source accuracy, missing information and reviewer workload | Underwriting and actuarial owners approve use |
| Policy service assistant | Correct answers, escalations and repeat contacts | Use the correct policy version; do not invent coverage |
| Fraud-review prioritization | Useful referrals, false positives and customer impact | A flag is not proof of fraud or authority to deny a claim |
Evidence to request from an insurance AI vendor
Request evidence for the same insurance line and intended use. Check data sources, subgroup performance, permissions, model changes and records of human review. A general document benchmark does not test your policy wording.
Count licensing, model usage, integration, review, monitoring and customer remediation in the operating cost. Track complaints and reopened claims alongside handling time. Faster work is not an improvement if errors rise.
Give reviewers authority to challenge AI outputs
Name who may accept, override or challenge each output. Define the evidence they see, the record kept and the customer escalation route. An approval button is not a control if the reviewer cannot inspect or reject the output.
Adoption data for insurers is in the AI in insurance statistics. For payer workflows involving patient data, see healthcare and pharma AI consulting.
Regulation and model risk
Model risk is the risk of harm from a model's errors or misuse. Paul organizes the evidence and review owners; qualified specialists determine the institution's legal, regulatory and validation requirements.
For credit scoring and other potentially high-risk uses, EU AI Act consulting covers classification and evidence. The scope depends on the system, market and intended use.
The NIST AI Risk Management Framework offers a voluntary structure for risk work. It does not certify a model or replace sector rules.
The NAIC overview of insurance AI explains that insurers remain responsible for applicable insurance requirements. Its model bulletin is not a single law automatically in force everywhere. Counsel and compliance owners must confirm local requirements.
Financial services decisions and deliverables
The agreed scope turns one priority workflow into a decision package. Each output names the evidence, owner and next review.
- A workflow map, cost baseline and executive sponsor.
- A data-flow and access map, including external model providers.
- A vendor evidence register and build-or-buy recommendation.
- A test plan covering errors, exceptions, unfair outcomes and escalation.
- A release, monitoring and incident plan with accountable owners.
- A recommendation to proceed, narrow the scope, fix the gaps or stop.
Delivery teams implement approved changes. For a project that already has approval, AI implementation consulting covers delivery oversight. Independent model validation, engineering and legal work remain separate scopes.
How the financial services engagement works
Start with the decision, institution type, markets and deadline. Agree access, outputs, specialist inputs and review dates before work starts.
The consulting rate is USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor. Send a non-confidential summary first. Do not include customer records, policyholder data or claims documents.
For ongoing executive ownership, consider a fractional Chief AI Officer. A recurring seat and a scoped advisory review address different needs.
Evidence for banks, fintechs and insurers
Ask advisers for the same workflow and risk level, not a general AI logo slide. Request a sample evaluation, an incident path and clear authority for customer-impacting decisions.
For named examples, see banks leading in AI, a sourced comparison of disclosed deployments. These examples are market context, not Paul's client results.
Paul's work covers senior decision support and governance. It does not establish actuarial qualifications, regulatory authority or named bank and carrier outcomes. The AI consultant selection guide helps compare remit, evidence and specialist coverage.
FAQ
What does financial services AI consulting cost?
Paul Okhrem charges USD 1,000 per hour, with an 80-hour minimum and USD 80,000 floor. The scope names the workflow, outputs and specialist inputs. Engineering, software and external specialists are separate unless included in writing. The minimum does not buy unlimited reviews or a staffed delivery program.
When should a bank hire an AI consultant?
Hire Paul Okhrem when a decision spans business value, technology and model risk, but lacks one clear plan. The engagement can compare vendors, set release gates and prepare board reporting. It does not replace the bank's risk owners. If only coding capacity is missing, hire an engineering team.
Should a fintech build or buy its AI capability?
Compare the feature's value, data access, control needs, expected usage cost and cost of switching providers. Paul can structure that decision and the evidence requests. A vendor demonstration alone is not enough. Product, security and bank-partner owners retain their approval roles.
Does this service include model-risk validation?
Not by default. Independent validation and financial-model review require qualified specialists and an agreed scope. Paul can identify the evidence, review gates and dependencies. That work does not authorize customer financial decisions or replace a legal opinion, regulatory adviser or the institution's internal approval process.
Can Paul review an insurer's claims or fraud AI proposal?
Yes. Paul can review workflow fit, evidence, running cost, integration and change controls. Track correct resolutions, false positives, reopened cases and customer impact. A fraud flag is not proof of wrongdoing. Claims, underwriting and actuarial specialists retain sign-off; this service does not approve autonomous claims decisions.
What should a financial services team include in its brief?
Send the institution type, workflow, markets, systems, decision owner and deadline. Describe the current problem and the choice you need to make. Keep customer, financial and policyholder records out of the form. Agree an NDA and secure sharing process before sending sensitive material.
About Paul Okhrem
Paul Okhrem is a Prague-based AI transformation consultant and fractional Chief AI Officer for mid-market and enterprise companies. He is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. He has built B2B and enterprise software since 2009.
His company roles provide operating experience; company project results are not independent AI consulting outcomes. Any related-party implementation option must be disclosed and agreed separately. Read the evidence and measurement method.
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