AI for insights and analytics
A relationship manager's book has clients who borrow and bank elsewhere, deposits that are quietly leaving, and facilities drawn to the limit. AI analytics reads the product, balance and facility files, compares each client with its sector, and answers the team head's question in figures that tie to the ledger.
These are the questions commercial banks ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| Which clients borrow from us and bank elsewhere? | Lending-only relationships | Clients holding only credit products, with the products similar clients hold and the estimated fee value of the gap. |
| Where are deposits leaving? | Deposit flight | Clients whose balances fell against their own baseline, by amount and pace, with the last contact. |
| Which relationships hold fewer products than their sector norm? | Products held against sector norm | Products per client against the median for its sector and size, ranked by the gap. |
| Whose facilities are nearly fully drawn? | Facility utilisation | Drawn against limit by client, with those over ninety percent and their trend. |
| Which relationships do not earn their capital? | Return per relationship | Income less cost of capital and servicing per client, with the loss-making ones and why. |
| Are relationship managers seeing their largest clients? | Coverage at cadence, by revenue | Revenue of clients met within cadence over revenue assigned, by manager and team. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
Where are deposits leaving the book?
Deposit balances are down $167.0m against each client's own baseline across 3 teams, on clients whose lending has not changed. Mid-market team accounts for $84.0m, 50% of the outflow.
| Mid-market team | $84.0m | 50% of the total |
| Real estate team | $52.0m | 31% of the total |
| Small business team | $31.0m | 19% of the total |
| The 3 lines sum to | $167.0m | 0 unexplained |
Which mid-market clients?
3 clients hold $76.0m of the $84.0m outflow in the Mid-market team, 90% of it. Two are lending-only relationships whose operating accounts were never with us, and the third moved its balances after a rate review elsewhere.
| Northfield Manufacturing | $31.0m | lending-only; operating account elsewhere |
| Grange Logistics | $27.0m | balances down 40% in two months; no contact logged |
| Ashcombe Foods | $18.0m | moved after a competitor rate offer |
| These 3 are | $76.0m | 90% of Mid-market team |
What does the team do with that?
Each client gets a named conversation about the whole relationship, not the loan. The manager goes with the products the client holds elsewhere and what similar clients hold with us, and logs the outcome.
| Mid-market relationship managers | Meet the three clients with the product gap analysis | This month |
| Team head | Review deposit flight and lending-only lists weekly | Weekly |
| Head of commercial | Put return per relationship in the quarterly review | Quarterly |
Each measure has one formula, one source and one meaning. They are computed per relationship manager and team and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Products held against sector norm | Products held ÷ median products held by clients of the same sector and size band | Product holdings file; client master | Relationships thinner than their peers |
| Lending-only relationships | Clients with credit exposure and no operating account, payments or fee products; exposure and return on each | Holdings file; facility file | Balance sheet used; fees earned elsewhere |
| Deposit flight | Average operating balance, last 3 months ÷ same months last year, per relationship; flagged below a stated ratio | Balance file | Cash management moving to another bank |
| Facility utilisation | Drawn ÷ committed, per facility, with trend | Facility file | Unused lines costing capital; clients drawing to the limit |
| Share of wallet by product | Bank's revenue from the client in the product ÷ estimated client spend on the product | Revenue file; estimates by source grade | Where the client's fee spend is going |
| Coverage at cadence, by revenue | Revenue of clients contacted within tier cadence ÷ total revenue | CRM; revenue file | Whether top relationships are being seen |
| Referrals between business lines | Referrals sent, accepted, converted, and revenue, by origin and destination | Referral log; revenue file | Whether one bank is working as one bank |
| Return per relationship | Revenue − cost of funds − expected loss − cost to serve, over capital used | Revenue, capital and cost files | Relationships that do not earn their capital |
| Portfolio load against capacity | Clients and required touches per relationship manager against available hours | CRM; client tiers | Portfolios too large to cover |
| Primary bank indicators | Share of client turnover seen through the account; payroll and tax payments present | Transaction file; client turnover | Whether the bank is the client's main bank |
Each measure is worked through, with the export it comes from and what to drop, in Relationship KPIs for commercial banking.
From your question and the measures declared for commercial banking, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the relationship manager or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Holdings | Product revenue per client sums to the revenue ledger |
| Balances | Relationship balances sum to the general ledger deposit total |
| Referrals | Sent = accepted + declined + pending; accepted = converted + lost + open |
| Coverage | Clients = covered + uncovered; each with one owner |
The exports commercial banks already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Deposit flight; coverage at cadence | Team heads; relationship managers | Weekly to monthly |
| Lending-only; products against norm; referrals | Head of commercial banking | Monthly |
| Return per relationship; facility utilisation | Head of commercial banking with finance and credit | Quarterly |
| Portfolio load | Head of commercial banking | Twice a year, and on every reassignment |
From your own book. If similar clients in the sector hold five products and this one holds two, the fee income those products earn from similar clients is the estimate, and it is labelled as an estimate. The tool shows the comparison set it used, so the manager can see whether the peers are fair.
It compares each client's balances with their own baseline month by month and flags a fall against pace, not just level. A client down forty percent in two months appears while the balance is still large, which is when the conversation is worth having.
Products held by client, balances by month, facility limits and drawn amounts, and the CRM activity file. Income by client turns product gaps into return per relationship. Client names can be replaced with identifiers before upload, and are on Enterprise.
Every balance figure carries a control total against the file it came from, and the deposit flight lines sum to the change in the book. If a client appears twice under two entities, the tool says so rather than counting it twice, and the roll-up is shown.
Lending-only relationship · Deposit flight · Facility utilisation · Norm penetration · Share of wallet · Coverage
Asset managers · Compliance · Construction and building materials · Consulting and advisory · Customer service · Distributors · Education · Finance and FP&A teams · Financial services · FMCG and CPG brands · Foodservice distributors · Freight brokers and 3PLs · Healthcare and med-tech · Hospitality · Industrial distributors · Industrial manufacturers · Insurance brokers · Investment banking · Law firms · Oil and gas services · Pharma · Procurement · Retail banking · SaaS · Sales teams · Shipping and logistics · Sports · Supply chain · Tax and accounting · Telecoms and connectivity · Trading · Wealth managers
Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.