How to use AI for conflict of interest checks (without missing anything)

Every professional services firm runs conflict checks. And almost every firm does them badly — or at least slowly. You're searching through old emails, scrolling CRM records, asking partners if they remember a name, and hoping nothing slips through the cracks. It's the kind of process that feels like it should have been fixed years ago.

Now it can be. AI doesn't just speed up conflict checks — it makes them fundamentally more reliable. Here's how to actually set this up, what to watch out for, and where the real gains are.

Why conflict checks are a perfect AI use case

Conflict of interest screening is essentially a pattern-matching and retrieval problem. You need to take an entity — a person, a company, a group of connected parties — and check whether your firm has any existing or prior relationship that could create a conflict.

That means searching across:

• Your CRM or practice management system
• Email archives
• Document management systems
• Companies House and other public registers
• Internal matter records and billing data
• Sometimes even personal knowledge held by individual partners

Humans are bad at this. We forget things, we misspell names, we don't think to check a maiden name or a subsidiary company. AI models — particularly large language models with retrieval-augmented generation (RAG) — are very good at this. They can search across unstructured data, handle fuzzy name matching, and surface connections that a keyword search would miss.

The practical setup: what you actually need

Let's be specific. Here's a realistic architecture for AI-assisted conflict checks in a UK professional services firm.

1. Centralise your data sources

Before AI can help, it needs access. The single biggest blocker for most firms isn't the AI — it's that their data lives in six different systems that don't talk to each other.

At minimum, you need to get the following into a searchable format:

• Client and matter records from your practice management system (Clio, LEAP, CCH, PracticeEvolve, etc.)
• Email metadata (not necessarily full email content — sender, recipient, subject lines, and dates can be enough)
• Document titles and metadata from your DMS
• Companies House data for corporate structures and directorships

You don't need a massive data warehouse project. A well-structured vector database (like Pinecone or Weaviate) connected to your existing systems via API can work. Some firms start even simpler — exporting CSVs from their CRM and indexing them.

2. Build a RAG pipeline for entity matching

This is where the AI does its work. When a new client or matter is opened, the system takes the key entities (individuals, companies, related parties) and runs them against your indexed data.

A good RAG pipeline for conflict checks should:

• Fuzzy match names — catching "Smith & Sons" vs "Smith and Sons Ltd", or "Catherine" vs "Kate"
• Expand entity searches — if you're onboarding ABC Ltd, also check its directors, parent company, and known subsidiaries
• Search semantically, not just by keyword — an LLM can understand that a document about "the vendor in the Elm Street transaction" might refer to the same party you're checking
• Return results with context — not just "match found" but the actual record, document, or email where the connection appears

Claude (Anthropic) and GPT-4o are both strong here. Claude is particularly good at careful, methodical analysis with long context windows — you can feed it a large set of retrieved records and ask it to assess whether a genuine conflict exists or whether it's a false positive.

3. Keep a human in the loop (seriously)

This is not optional. AI should surface potential conflicts and present the evidence. A qualified professional — your COLP, compliance officer, or supervising partner — makes the final call.

The SRA, FCA, RICS, and every other UK regulator expects a human decision on conflicts. What AI does is make sure that human has all the information they need, fast.

A practical workflow looks like this:

1. New matter opened → entities entered (or extracted automatically from an engagement letter using AI)
2. AI runs conflict search across all indexed data sources
3. Results categorised: clear conflicts, possible conflicts, no conflicts found
4. Possible and clear conflicts routed to the responsible person with full context
5. Decision recorded and logged for audit trail

Where firms are getting real results

Solicitors are the obvious use case — SRA rules on conflicts are strict and the consequences of getting it wrong are severe. Firms using AI-assisted conflict checks report cutting the process from 24-48 hours to under 30 minutes, with fewer false negatives.

Accountants dealing with audit independence rules are another strong fit. Checking whether anyone in the firm (or their family members) has a financial interest in a prospective audit client is exactly the kind of thorny, multi-source search that AI handles well.

IFAs and mortgage brokers regulated by the FCA can use the same approach for connected-party checks and identifying potential conflicts in referral arrangements.

Recruiters placing candidates at competing clients — particularly in retained search — can use AI to flag situations where placing a candidate could breach an exclusivity agreement or damage a client relationship.

What to watch out for

Data protection. You're processing personal data, so your RAG pipeline needs to comply with UK GDPR. Make sure you have a lawful basis (legitimate interest is usually appropriate for conflict checks), and that data is stored securely. If you're using cloud-based AI models, check where the data is processed — and whether the provider uses your data for training. Both OpenAI and Anthropic offer enterprise tiers that don't train on your data.

False confidence. AI can miss things if your underlying data is incomplete. If half your matters from 2019 never made it into the CRM, AI won't find them. Garbage in, garbage out. Run parallel manual checks for the first few months while you build confidence.

Over-engineering. You don't need a custom-built platform to start. Some firms begin with a simple setup: export client lists, index them, and use Claude with a system prompt designed for conflict analysis. You can iterate from there.

A simple way to start this week

If you want to test this before committing to infrastructure:

1. Export your client and matter list from your practice management system as a CSV
2. Upload it to Claude (Pro or Team plan) as a file
3. Prompt: "I'm onboarding a new client: [name, company, related parties]. Search the attached data for any existing or prior relationships with these entities or closely related parties. Consider name variations, associated companies, and indirect connections. Present any potential conflicts with the relevant records."
4. Review what comes back

You'll be surprised how effective this is, even as a manual process. It's not a replacement for a proper automated pipeline — but it proves the concept and usually surfaces at least one connection that a keyword search would have missed.

The bottom line

Conflict checks are high-stakes, high-effort, and high-frequency. That's the trifecta for AI automation. The firms that get this right don't just save time — they reduce regulatory risk and catch conflicts that manual processes miss entirely.

You don't need to build everything at once. Start with a simple test, prove it works for your data, and then invest in proper infrastructure. The important thing is to stop relying on partner memory and Ctrl+F.