Charge Data as a Concentration Risk Tool: A Practical Guide

Every time a lender takes security over an asset, a charge is registered at Companies House. These registrations — publicly available, structured, and continuously updated — form one of the richest datasets available to anyone who wants to understand the UK bridging and development lending market. Most lenders use charge data only to verify security on their own facilities. Far fewer use it as a tool for understanding market-wide lender exposure.

This article explains how to interpret charge data, what concentration risk signals it can reveal, and how to build a systematic approach to charge-based lender intelligence.

What Charge Data Actually Contains

A registered charge at Companies House includes: the company number of the entity granting the charge, the name of the person entitled (i.e., the lender), the date of creation, the charge type (fixed, floating, or debenture), the assets charged, and the status (outstanding or satisfied). Crucially, it also includes the charge code — a unique identifier that allows you to track individual facilities across time.

What charge data does not directly tell you is the loan amount, the LTV, the interest rate, or the maturity date. These remain private. However, the frequency, pattern, and counterparty relationships visible in charge data are powerful enough to draw significant conclusions about lender activity, borrower behaviour, and market concentration.

Reading Lender Concentration from Charge Volume

The first metric to extract from charge data is charge volume by lender. By aggregating all outstanding charges across Companies House for a specific lender entity, you can estimate their relative market activity — how many live facilities they hold, how this has changed over time, and whether volume is growing or contracting.

More interesting is cross-lender comparison. When you map the charge volume of ten or twenty active bridging lenders simultaneously, concentration patterns emerge. You may find that a small number of lenders account for a disproportionate share of charges on SPVs controlled by a particular sponsor group. This is concentration risk made visible.

For a lender reviewing a new application, the question is not just "what security can we take?" but "how many other lenders already have charges on entities controlled by this sponsor?" If a sponsor's SPV network has 15 outstanding charges across 8 different lenders, the aggregate debt burden and the likelihood of cross-default events is significantly higher than the origination file will suggest.

Satisfaction Events as a Signal

Charge satisfaction — the registration of a charge being repaid and released — is as informative as charge creation. A lender whose charges are consistently satisfied within the facility term is a market participant with a healthy book and borrowers who are performing. A lender with an unusually high proportion of outstanding charges relative to their historical creation volume may be experiencing refinancing challenges across their portfolio.

For borrower assessment, charge satisfaction history is a direct indicator of repayment track record. An SPV that has created and satisfied 12 charges over five years has demonstrated an ability to exit facilities on schedule. An SPV that created 4 charges and has zero satisfaction events should prompt questions about how those facilities were resolved — refinanced, extended, or enforced.

Building a Charge Intelligence Workflow

A practical charge intelligence workflow for a bridging lender has three stages. At origination, pull all outstanding and historic charges for the target SPV and all entities controlled by the same parent company network. Calculate total outstanding charge count, identify lenders already in the capital stack, and flag any charges that were created and never satisfied.

During the loan term, monitor for new charge registrations on the target SPV and related entities. A new charge registered during your facility period — particularly a floating charge or debenture — may represent a covenant breach if your facility documentation restricts further borrowing. Automated monitoring is the only practical way to catch these events in time to act.

Across the loan book, run periodic concentration analysis to identify lenders, sponsors, or sectors where your aggregate charge exposure is highest. This does not require any data sharing with competitors — it requires only that you systematically analyse the public charge data that Companies House makes available to everyone.

Loan Intel ingests and processes Companies House charge data continuously, mapping charges to lender entities, SPV structures, and parent company networks to produce the cross-market intelligence picture that individual lenders cannot construct on their own. The result is a lender concentration score for every active borrower — updated in near real-time with every new filing.