AI for insights and analytics
A client that paid 38 million in fees over three years and gave the bank five percent of it looks active in the CRM and is failing on every measure that involves the wallet. AI analytics reads the revenue, wallet estimate and CRM files, and answers the sector head's question: where is our share low, and who has not seen the CFO.
These are the questions investment 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 |
|---|---|---|
| Where is our share of the client's fee wallet lowest? | Share of client fee wallet | Fees earned over estimated fees paid, by client and product, ranked by the gap in dollars. |
| Which pitches turned into mandates? | Pitch to mandate conversion | Mandates won over pitches made, by sector team and product, with the clients pitched repeatedly and never won. |
| Which large wallets have not been covered recently? | Coverage recency, wallet-weighted | Days since senior contact, weighted by wallet, with the largest untouched clients. |
| Do we know the right people at each client? | Senior contact breadth | Distinct senior contacts met per client against the norm, with the clients where all contact is below board level. |
| Where is balance sheet committed without fee return? | Return on balance sheet committed | Fees over lending committed by client, with the relationships below the hurdle. |
| Which clients use us for one product where peers use three? | Products per client against norm | Products per client against the sector norm, with the candidates and the wallet at stake. |
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 we under-earning against the client wallet?
The gap between estimated fee wallets and fees earned is $195.0m across 3 sector teams, counting only clients where we hold a relationship. Industrials team carries $96.0m, 49% of the gap.
| Industrials team | $96.0m | 49% of the total |
| Consumer team | $61.0m | 31% of the total |
| Healthcare team | $38.0m | 19% of the total |
| The 3 lines sum to | $195.0m | 0 unexplained |
Which industrials clients?
3 clients account for $84.0m of the $96.0m gap in the Industrials team, 88% of it. At each, the bank has balance sheet committed, has pitched more than three times, and has not met the chief financial officer in the last six months.
| Meridian Industrial Group | $36.0m | 5% wallet share; 150m committed; 4 pitches, 0 mandates |
| Calder Engineering | $29.0m | 8% wallet share; CFO last seen 11 months ago |
| Vantage Materials | $19.0m | one product; peers hold three |
| These 3 are | $84.0m | 88% of Industrials team |
What does the sector head do with this?
Reset the coverage plan on the three: a CFO meeting owned by a managing director, a product plan that matches the wallet, and a decision on whether the committed balance sheet stays.
| Industrials managing directors | Secure CFO meetings at Meridian, Calder and Vantage | This quarter |
| Head of coverage | Review wallet share and recency monthly by sector | Monthly |
| Capital committee | Revisit the Meridian commitment against fee return | Next committee |
Each measure has one formula, one source and one meaning. They are computed per coverage officer and sector team and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Share of client fee wallet | Bank's fees from the client ÷ client's estimated total fees paid, trailing 3 years, by product | Revenue ledger; third-party fee data | Whether a client is a success or a missed opportunity |
| Pitch to mandate conversion | Pitches mandated to the bank ÷ pitches decided, by sector team and product | Pitch log | Which teams pitch well, and which pitch often |
| Coverage recency, wallet-weighted | Wallet of clients with a senior touch within cadence ÷ total covered wallet | CRM; wallet estimates | Whether the largest wallets are being seen |
| League table position against client wallet | Bank's rank by fees among its covered clients, by sector and product | Fee data; coverage list | Where the bank ranks where it matters |
| Senior contact breadth | Distinct C-suite and board contacts with activity in 12 months, per client | CRM | Relationships resting on one person |
| Products per client against norm | Products with revenue in 3 years ÷ norm for client type | Revenue ledger | Single-product relationships |
| Revenue concentration | Top twenty clients' share of fees; largest single client | Revenue ledger | Dependence on a few mandates |
| Pipeline: mandated and pitched | Expected fees by stage and expected close, with age in stage | Deal pipeline | What next year's revenue rests on |
| Transition continuity | Days from coverage change to first senior touch; wallet share before and after | CRM; coverage history | Clients lost in handovers |
| Return on balance sheet committed | Total client revenue ÷ capital committed to the client | Revenue ledger; lending book | Lending that did not bring the ancillary business |
Each measure is worked through, with the export it comes from and what to drop, in Coverage KPIs for investment banking.
From your question and the measures declared for investment 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 coverage officer or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Wallet share | Client fees by product sum to the revenue ledger |
| Pitch conversion | Pitches = mandated to us + mandated elsewhere + not proceeded + open |
| Coverage | Every covered client has one lead officer |
| Return | Client revenue and capital tie to the finance totals |
The exports investment 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 |
|---|---|---|
| Recency; senior contact breadth; transitions | Sector heads; head of coverage | Monthly |
| Pitch conversion; pipeline | Sector and product heads | Monthly |
| Wallet share; rank among clients; products per client | Head of coverage | Quarterly |
| Return on balance sheet; concentration | Head of coverage with finance and credit | Quarterly |
From the file you provide, whether that is a vendor's fee estimate, a public deal-fee model or your own. The tool never estimates a wallet itself. Fees earned over the estimate is computed by client and product, and the page says which estimate it used and when it was dated.
Because a coverage report that counts meetings treats a client paying two million and one paying forty million alike. Weighting days since senior contact by wallet puts the large, unseen client at the top, which is where the sector head's attention belongs. The unweighted figure is shown beside it.
Yes. On Enterprise, client names are replaced with identifiers before anything leaves your side, and the mapping stays with you. The tool works on the identifiers; the sector head reads the results with the names restored on their own screen.
As a fact to set beside fees: lending committed by client from the credit file, fees earned from the revenue file, and the ratio between them against the hurdle you set. Whether a commitment is strategic is a committee decision; the tool makes sure the figures are on the table.
Fee wallet · Pitch conversion · Contact recency · Share of wallet · Concentration · Coverage
Asset managers · Commercial banking · 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 · 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.