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Blog · Alternatives and comparisons · Investment banking

How to choose analytics software for investment banking: questions, data and traps

How investment banks should choose analytics software: start from the questions, check the data you hold, ask vendors ten questions, avoid the traps.

By CoviragePublished 24 September 20264 min read
The short answerStart from the questions investment banks ask every month, not from features. List the exports you already hold, ask every vendor what it needs before the first answer and whether its AI calculates figures, and check that every total reconciles. Then compare the first-year cost, all in.

Most buying decisions for analytics start from a feature list. For investment banks the better start is the questions that come back every month, the files already on hand, and the traps that make a tool look right in a demonstration and wrong in the first board meeting.

Start from the questions

The question The measure behind it
Where is our share of the client's fee wallet lowest? Share of client fee wallet
Which pitches turned into mandates? Pitch to mandate conversion
Which large wallets have not been covered recently? Coverage recency, wallet-weighted
Do we know the right people at each client? Senior contact breadth
Where is balance sheet committed without fee return? Return on balance sheet committed
Which clients use us for one product where peers use three? Products per client against norm

Any tool you consider should answer these from your data, not from a sample. Ask to see it.

The data you already hold

  • Revenue ledger
  • Third-party fee data
  • Pitch log
  • CRM
  • Wallet estimates
  • Fee data
  • Coverage list
  • Deal pipeline

If a vendor needs a warehouse built before it can read these, count that in the cost and the time.

Ten questions to ask any vendor

  1. What does it need in place before the first answer? A warehouse, a data model, a modelling language, a partner? Ask for the list and the typical weeks.
  2. Who does the setup, and who maintains it? Your team, the vendor, or a partner, and what that costs after year one.
  3. Does the AI calculate figures, or choose from computed ones? A language model that writes queries or code can produce a plausible wrong number. Ask what it is allowed to do.
  4. Does every total reconcile to a control figure? Ask to see a bridge that does not sum and what the product does about it.
  5. Can every figure be opened to its rows? An answer nobody can check becomes a debate in the meeting.
  6. What does it cost in the first year, all in? Licences, consumption, implementation, modelling and training, not only the seat price.
  7. How does data arrive, and who holds credentials? A file your systems already export, a scheduled drop, or a live connection with the vendor holding keys.
  8. What happens to the data, and where is it stored? Residency, retention, deletion, and whether names can be replaced with identifiers.
  9. Can we see it on our own data before we sign? A demonstration on a sample dataset tells you little about your own.
  10. What does the tool do when it cannot answer? It should say so. A confident guess does more harm than no answer.

Checks specific to investment banking

Ask whether the tool enforces these, and what it does when they fail:

  • 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 traps

Pitch conversion. Most pitch logs record that a pitch happened and never what became of it.

Wallet-weighted recency. Meeting counts reward activity with small clients who are easy to see.

Return on balance sheet. The loan was justified by future fees. Whether the fees came is seldom checked client by client.

Measures to leave out

Meetings logged. Replace with wallet-weighted recency and contact seniority.

Market-wide league table rank, alone. Rank among covered clients is the commercial figure.

Pitches made. A cost until conversion is known.

A scorecard

Criterion Weight Tool A Tool B Covirage
Answers our six questions on our own data High
Time to the first answer High
Needs a warehouse or data team Medium
AI calculates figures, or only explains computed ones High
Every total reconciles; figures open to rows High
First-year cost, all in Medium

Where Covirage fits

Covirage reads the exports above, answers the questions with figures our tools compute and check, and is set up for you within a week. See analytics software for investment banking compared, AI analytics for investment banking and Covirage for Investment banking.

For the measures in full, with formulas and exports, read Coverage KPIs for investment banking.

Questions people ask

What should investment banks look for in analytics software?

The answer to their own questions, from the data they already hold, with every figure reconciled. Features matter less than what the tool needs before the first answer and who maintains it.

Is a BI suite enough for investment banks?

It can be, with a warehouse and someone to build and maintain the model. Without them, the dashboard shows what changed and the explanation is still an analyst's job.

What data do investment banks already hold?

Usually: revenue ledger, third-party fee data, pitch log, crm, wallet estimates, fee data. Most analytics questions in this industry can be answered from those exports.

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