Illustrative demonstration · Synthetic dataOrganisations, transactions, documents, figures and outcomes shown here are fictional or modelled for demonstration purposes. No client, bank or confidential data is used. Results are illustrative, not actual client outcomes or professional advice.
Industry case · Financial services · Interactive demo
From transaction noise to fee income and retained customers.
A large retail bank, about nine billion payments a year across UPI, cards, IMPS, NEFT and RTGS, analysed for exception load, fraud pressure and reconciliation health, then converted into five costed moves.
Headline insightUPI is 71% of transaction count but 22% of value; RTGS is 0.3% of count and 41% of value. Queues worked by alert age instead of value-at-risk leave the highest-value channel with the slowest resolution, and the fraud data shows the same inversion: the rarest typology converts to real fraud at nearly three times the rate of the noisiest one.
1Read the operation2Price the moves3The strategy
1 · Read the operationWhere does it actually hurt?
Six views, one dataset, and each ends in a move
Monthly volume and exception rate
Transactions processed per month (bars, millions) against exception rate (line, %)
Transactions, millionsException rate %
Insight. The festive surge (Oct–Nov) lifts volume ~30%; in the baseline, exceptions rose with it. After auto-repair of the top five failure codes went live in June, exceptions fall while volume grows: festive season absorbed without adding headcount. → Extend auto-repair beyond the first five codes (Move 3).
Channel mix: count vs value
Share of transaction count and settled value by payment rail, %
Share of countShare of value
Insight. The count/value inversion is the structural fact of the book. Staffing follows count; risk follows value. → The RTGS desk and value-weighted queues (Moves 1–2) exist to close that gap.
Fraud alerts vs confirmed fraud, by typology
Annual alerts and confirmed cases, thousands · right-hand figure is the conversion rate
AlertsConfirmed fraud
Insight. Velocity rules generate the most alerts (88k) at an 11% hit rate; social-engineering and mule alerts are far fewer (38k) but convert at 30%. Analyst time is priced by the wrong queue. → Re-weight queues by expected loss (Move 1).
Exception intensity by channel and time of day
Exception rate, % of channel volume, by three-hour window
LowHigh
Insight. Three hotspots, three causes: UPI 18–24h (evening consumer peak, beneficiary-bank timeouts), NEFT 09–15h (batch congestion), RTGS 15–18h (cut-off pressure). One "exception team" is actually three problems. → Retry orchestration for UPI (Move 4), batch smoothing for NEFT (Move 5), a value desk for RTGS (Move 2).
The failed-payment funnel: where value leaks
Average day: what happens to the ~88k transactions that fail on first attempt, thousands
Insight. ~9k payments a day end the day unresolved, and an estimated 2.4k customers sit in the churn-risk band (two or more failures in 30 days). Failed payments are a revenue problem: lost fee income today, lost customers tomorrow. → Smart retry (Move 4) attacks the top of this funnel, where recovery is cheapest.
Top exception codes and repair status
Highest-volume failure codes last quarter
Code
Description
Monthly vol.
Avg resolution
Value at risk / item
Repair status
U30
Beneficiary bank timeout (UPI)
241,000
4.2 min
₹2,400
Auto-reversed
R03
Account number mismatch (NEFT)
88,400
6.1 hrs
₹48,000
Assisted repair
C57
Card settlement file variance
31,700
1.4 days
₹9,100
Assisted repair
RT9
RTGS cut-off breach
2,150
2.8 days
₹41,00,000
Manual · high value
Insight. Read the last two columns together: the smallest queue carries a per-item value three orders of magnitude above the biggest one, and resolves slowest. Sorting work by queue length optimises exactly the wrong thing.
2 · Price the movesWhere should effort go first?
Toggle moves in Section 3; the matrix, the bridge and the totals respond
Effort vs modelled annual value
Bubble size = confidence in the estimate · dashed = enabler, value counted within other moves
Insight. Queue re-weighting and the RTGS desk are process changes, not builds, with value in weeks. Smart retry is the largest prize and a genuine engineering effort; sequence it after the quick wins fund the credibility.
Modelled annual value bridge
How the selected moves stack, ₹ crore per year
3 · The strategyFive moves, process changes before builds
Click a move to include or exclude it from the programme
Guardrail · GovernanceAutomation with audit trails and human sign-off on valueEvery auto-repair and retry is logged, reversible and capped; anything above the value threshold routes to a person. Model and rule performance is reviewed monthly against false-positive load and customer complaints. How: the same discipline the June rollout used: expand coverage, never relax the controls.
