How AI Slips Into Insurance Without Touching Risk

U.S. insurance brokerages sit in the middle of more than $1.4 trillion in annual premium flows, according to the Insurance Information Institute, processing payments between insureds, carriers, and trust accounts.

Those payments arrive through multiple channels, including paper checks, ACH transfers, and carrier remittance files, and require reconciliation before commissions are distributed and accounts are closed. As transaction volumes increase with brokerage scale, firms have begun deploying automation in payments and receivables ahead of underwriting or claims systems.

M3 Insurance just announced a partnership with SimplePin to deploy software focused on insurance receivables, payment processing, and reconciliation. In the release, Jamin Friedl, senior director of finance at M3, said the platform would reduce manual processing and improve the payment experience for clients and carrier partners. The deployment scope is limited to financial operations.

Payments and reconciliation as deployment targets

Insurance brokerages process premium payments that move between insureds, carriers, and internal trust accounts. Those payments arrive via multiple channels (paper checks, ACH transfers, carrier statements, lockbox files) and often lack standardized remittance data. Matching funds to invoices and allocating commissions has historically required manual reconciliation.

SimplePin’s platform is designed to automate portions of that process. According to the company’s product documentation, its SimpleMatch system uses optical character recognition and machine-learning techniques to extract data from unstructured documents, normalize inputs, and match payments to receivables. Transactions that cannot be matched with sufficient confidence are routed to exception queues for manual review.

The system proposes matches and flags discrepancies. It does not publish decisions related to coverage, risk evaluation, or claims outcomes.

Broader market context for receivables automation

The M3 deployment aligns with broader investment in accounts-receivable automation across industries. Market research firms estimate the global accounts receivable automation market at approximately $2.8 billion in 2024, with projected compound annual growth in the low double digits.

Vendors such as Billtrust, HighRadius, and Bottomline market similar tools that apply machine learning to cash application and reconciliation. Billtrust, for example, states that its platform uses AI to match payments and reduce manual exceptions in accounts receivable workflows.

Insurance brokerages differ from other industries in how receivables are handled. Premium funds are often held in trust, commissions are distributed across multiple parties, and reconciliation errors can carry regulatory consequences. Automation in this area is therefore typically implemented with audit trails and human override.

Vendor-driven deployment model

M3 has not disclosed development of proprietary machine-learning models. The SimplePin platform integrates with existing agency management systems, including Applied Epic and Vertafore AMS360, allowing payment data to be written back into core records and general ledger systems.

This deployment model is consistent with how brokerages typically adopt new technology: through certified third-party platforms that integrate with existing systems of record. Responsibility for model development, maintenance, and security resides with the vendor.

M3 has previously described its use of advanced analytics tools, including AI-assisted insights delivered through Tableau dashboards, to support client advisory and internal analysis. Public materials do not describe the use of AI systems in underwriting or claims operations.

Regulatory constraints and operational pressure

Audit and regulatory bodies have begun addressing the use of automated tools and AI in financial reporting. The Public Company Accounting Oversight Board has stated that technology-assisted analysis can improve audit quality, while emphasizing the need for controls, documentation, and human oversight.

Guidance from audit and accounting organizations focuses on traceability, explainability, and reviewability of automated outputs. Systems that assist with classification or matching are treated differently from systems that make determinations affecting financial statements or regulated outcomes.

As brokerages grow through consolidation and expanded service lines, transaction volume increases faster than finance headcount. Payment processing and reconciliation workloads scale with volume, not with underwriting activity.

The M3 deployment applies automation to those scaling pressures. The system operates within defined financial workflows and does not extend to functions that determine coverage, pricing, or claims outcomes.