Insurance & Financial Services

An insurance agency or a financial-services firm runs on documents, reconciliations and renewals, under rules that leave little room for guessing. Commission statements arrive from several insurers, each in its own format. Client documents arrive incomplete. A client who is about to leave at renewal sends signals months before. All of it involves personal data that has to stay under control. We design and build systems that read, match and flag, where every answer has a source and the data stays where your regulation requires.

Where does the work pile up?#

• Commission reconciliation. Each insurer sends its own commission report. Matching it to the agency’s production records, policy by policy, and chasing the differences is monthly work that rarely ends.
• Renewals and retention. The signs that a client is about to move are scattered across systems, for example a claim that went badly, a change in coverage or a direct debit that bounced.
• Client documents. Forms, ID copies and claim papers arrive by email and WhatsApp, often incomplete, and someone checks each file before it can move on.
• Answers for clients. Policy terms and internal procedures are long, and staff answer from memory.
• Collections and cash. In credit and finance firms, a borrower whose payment pattern changes is a risk long before the arrears.

Where does AI fit, and where does it not?#

• Matching at volume. The system matches every commission line to its policy and sorts the differences by insurer and by amount, for someone on the team to chase. It is the same problem as reconciling a bank statement.
• Early signals before renewal. A weekly list of clients whose behavior is changing, with the reason shown, so the agent calls in time.
• Document checks. The system reads each file on arrival and checks what is missing before it reaches a desk.
• Search over policies and procedures, in plain language, with the clause the answer came from.

Where it does not fit: deciding on coverage, underwriting or credit for a client. Those decisions stay with the people who hold the authority, and our systems prepare their information, never their decision.

Which of our solutions fit?#

• Month-end and bank reconciliation: the matching engine, as it is documented on bank and card lines.
• Receivables anomaly detection: payers whose behavior is starting to drift, on a weekly list.
• Customer & Risk Intelligence: churn analysis and behavioral classification on real data.
• Knowledge & Document AI: search and document processing over your own content, with answers that cite their sources.
• Secure & Air-Gapped AI: language models and search that run inside your network when client data may not leave it.
• The weekly management brief: the week’s new business, collections and open items on one page.

How does an engagement start?#

With one reconciliation or one list. Commission reconciliation is usually the first step: the rules are clear, the volume is high, and the result is measured in shekels found and hours saved. In the fixed-price scoping stage we take a few months of real statements and your records, and write down what can be matched automatically. The same stage decides where the data lives, in your cloud account or inside your network, with your compliance officer in the conversation. One success measure is agreed before anything is built. How we work sets out the stages.