AI for insights and analytics
A client that trades 45 percent of its inquiries by count and eight percent by size ranks well on the venue report and is sending every large ticket elsewhere. AI analytics reads the inquiry and trade files, weights by size, and answers the head of sales: who is really trading with us, and who has gone quiet.
These are the questions desks 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 trade with us by count but not by size? | Size-weighted hit ratio | Hit ratio by count and by notional per client and product, with the gap between them ranked. |
| Who has gone quiet? | Quiet client list | Clients whose inquiries fell against their own baseline, by prior revenue, with the last contact. |
| What share of each client's inquiries do we see? | Inquiry share | Our inquiries against the client's reported total where known, by product. |
| Are clients trading the products we are axed in? | Axe hit rate | Trades in axed products over axes shown, by client and desk. |
| What did we trade that no salesperson had sent? | Traded unsent | Trades with no prior inquiry or axe from us, by client, with the notional. |
| How fast do we answer, and does it matter? | Response time on inquiries | Time from inquiry to quote by desk, against hit ratio in the same buckets. |
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 is the size-weighted hit ratio far below the count?
Notional we quoted and did not win is $4800.0m across 3 desks, on clients whose count hit ratio is above 40 percent and size hit ratio below 15. Rates desk holds $2400.0m, 50% of it.
| Rates desk | $2400.0m | 50% of the total |
| Credit desk | $1500.0m | 31% of the total |
| FX desk | $900.0m | 19% of the total |
| The 3 lines sum to | $4800.0m | 0 unexplained |
Which rates clients?
3 clients account for $2200.0m of the $2400.0m lost notional on the Rates desk, 92% of it. Every trade over ten million at each went elsewhere.
| Aldergate Capital | $1100.0m | 45% by count, 8% by size |
| Northbridge Asset Management | $700.0m | 48% by count, 11% by size |
| Sable Macro Fund | $400.0m | 41% by count, 6% by size |
| These 3 are | $2200.0m | 92% of Rates desk |
What does the desk change?
Find out where the large tickets go and why: pricing, response time or a relationship elsewhere. The salesperson takes the size split to the client; the desk head reviews response time on tickets over ten million.
| Rates salespeople | Discuss the large-ticket split with the three clients | This fortnight |
| Head of rates trading | Review response time on inquiries over 10m | This week |
| Head of sales | Report both hit ratios weekly, by client tier | Weekly |
Each measure has one formula, one source and one meaning. They are computed per salesperson and product and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Hit ratio by client and product | Inquiries traded ÷ inquiries received, per client, product and channel | Inquiry log; blotter | Where the desk wins and loses |
| Size-weighted hit ratio | Notional traded ÷ notional inquired | Inquiry log; blotter | Clients who trade small with the desk and size elsewhere |
| Inquiry share | Client inquiries seen ÷ estimated client inquiries in the product | Inquiry log; venue or client-stated data | Whether the desk is shown the flow at all |
| Quiet client list | Clients whose inquiries in the last 20 days are below a stated fraction of their own norm, where desk volume is normal | Inquiry log | Flow that has moved to another dealer |
| Client tiers by flow and profitability | Tier from trailing volume and revenue after cost of capital and hedging | Blotter; revenue attribution | Where balance sheet and attention should go |
| Axe hit rate | Axes traded by the client ÷ axes received, within the window | Axe log; distribution records; blotter | Whether the distribution lists work |
| Traded unsent | Clients trading the axed instrument, right side, in the window, without having been sent it | Axe log; blotter | Clients missing from the lists |
| Coverage recency by value | Revenue of clients with a two-way contact within cadence ÷ revenue | CRM or chat logs; revenue | Top clients nobody has spoken to |
| Revenue concentration | Top ten clients' share; largest client share, by product | Revenue attribution | Dependence on a few accounts |
| Response time on inquiries | Median seconds or minutes to quote, by product and channel; hit ratio by response band | Inquiry log with timestamps | Trades lost to speed, not price |
Each measure is worked through, with the export it comes from and what to drop, in Client KPIs for sales and trading desks.
From your question and the measures declared for trading, 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 salesperson or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Hit ratio | Inquiries = traded + traded away + passed + no quote |
| Axes | Matched trades are a subset of the blotter; each trade matches at most one axe |
| Tiers | Every client is in one tier; client revenue sums to the desk total |
| Coverage | Clients = covered + uncovered |
The exports desks 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 |
|---|---|---|
| Quiet client list; coverage recency | Head of sales; salespeople | Weekly |
| Hit ratio, both weightings; response time | Head of sales with head of trading | Weekly to monthly |
| Axe hit rate; traded unsent | Head of sales | Monthly |
| Tiers; inquiry share; concentration | Head of sales and trading | Quarterly |
Because count says how often the client trades with you and size says how much. A client at 45 percent by count and 8 percent by size is testing prices on small tickets and trading the large ones elsewhere. Both are computed from the inquiry and trade files, and the difference is the finding.
One whose inquiries fell against its own baseline, not a fixed number, ranked by the revenue it produced before. The list carries the last logged contact, so the head of sales can see whether silence was noticed. It is a list to work through, with an owner per row.
The files are read where you choose to run it, and client identities can be replaced with identifiers before any upload on Enterprise. No figure is sent to a model to compute; the model only writes the sentence around a result a tool has already produced.
Every measure is computed per desk, per salesperson and in total, and the totals reconcile. A client that trades rates and credit is one client in the tier and two rows in the desk view, and the page says which.
Hit ratio · Quiet client · Axe hit rate · Traded unsent · Flow share · Concentration
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 · 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 · 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.