Use case #0001

How Risk Reporting AI assembles a CRO dashboard in under 60 seconds

A Chief Risk Officer who opens a risk dashboard at 8 AM should see the risk position as it stands at 8 AM — not as it stood when someone last ran the weekly report. The Risk Reporting Agent AI assembles the CRO dashboard in under 60 seconds from live data feeds across six operational systems, producing a single-view risk picture that reflects the portfolio's current state rather than its state at the last reporting cycle.

A Chief Risk Officer who opens a risk dashboard at 8 AM should see the risk position as it stands at 8 AM — not as it stood when someone last ran the weekly report. The Risk Reporting Agent AI assembles the CRO dashboard in under 60 seconds from live data feeds across six operational systems, producing a single-view risk picture that reflects the portfolio's current state rather than its state at the last reporting cycle.

Why the CRO dashboard is almost always stale — and why that matters

A risk dashboard built from manually extracted, periodically refreshed data is almost always behind reality. The portfolio NPA figure shown on a Monday morning dashboard was computed on Friday — and it reflects the portfolio as it was on Thursday, because the Friday run used Thursday's CBS snapshot. An account that crossed the 90-day NPA threshold on Saturday is not on the Monday dashboard. A large LAP account that was written off on Friday afternoon is not on the Monday dashboard. The CRO is making risk-based decisions from a two-to-four day old picture of the portfolio.

In a stable, slowly changing portfolio, this lag is tolerable. In a portfolio where NPA classification runs daily and collections activity is continuous, a four-day dashboard lag is a governance failure. The CRO cannot perform their function — assessing whether the institution's risk position is within appetite, escalating emerging risks to the Board, directing management attention to deteriorating segments — from stale data.

The Risk Reporting Agent AI connects directly to the live data sources — the CBS, the Provisioning AI's classification database, the Stress Testing AI's scenario outputs, the collections system, and the Bureau pull log — and assembles the dashboard in under 60 seconds, on demand, showing the position as it stands at the moment of assembly.

"A risk officer whose dashboard is four days old is not managing risk — they are reviewing history. The Risk Reporting AI shows the present."

The data sources that feed the dashboard — and their latency

Core Banking System
Primary balance and payment data

Outstanding balances, payment status, NACH results, EMI schedule

CBS is the authoritative source for account balances and payment behaviour. The Risk Reporting AI pulls the live CBS balance for every NPA account and the payment register for the current day — not a nightly snapshot. Any NACH result received since midnight is included in today's dashboard.

Real-time
API pull
Provisioning AI
Classification and provision data

Current NPA classification, DPD count, provision required and held, 75-day watch list

The Provisioning AI runs its classification check at midnight every day and makes the results available via API. The CRO dashboard pulls the current classification for every account — post the overnight run — giving the CRO the day's freshest NPA picture before they open their laptop.

Daily
Post midnight
Stress Testing AI
Scenario output and CRAR position

Latest stress scenario results, current CRAR, rate shock impact at current portfolio composition

The most recent stress test scenario output is included in the CRO dashboard, showing the current CRAR and the CRAR under the RBI benchmark 200bps shock. If an on-demand stress test was run since the last dashboard assembly (triggered by an RBI rate change or CRAR proximity alert), the latest result is used.

Latest run
Auto-refreshed
Collections System
Early bucket DPD and collection activity

DPD 1–90 distribution, collection contact rates, PTP rates, broken PTP counts, NACH failure rates

The collections system provides the leading indicators of future NPA: how many accounts are in the 60–89 DPD range (approaching the NPA threshold), what the current month's NACH failure rate is, and whether collection activity has resulted in PTPs at an acceptable rate. These are forward-looking credit quality signals.

Same-day
Intraday
Risk Pricing AI
NIM position and pricing metrics

Current portfolio NIM, yield by segment, rate-shock NIM compression, pricing adequacy vs risk

The Risk-Based Pricing AI's current NIM estimate is included in the dashboard, showing whether the portfolio is generating adequate margin relative to the risk being carried. The NIM figure is segment-level — the CRO can see which segments are generating adequate yield and which are not.

Current day
Market rates live
Origination AI
Pipeline quality and new disbursement risk

Current week disbursements, average CIBIL score, FOIR distribution, and score quality vs portfolio average

New disbursements create future risk — the CRO needs to see not just the current portfolio but the risk characteristics of what is being added to it. The Origination AI provides the current week's disbursement quality metrics: average CIBIL score, FOIR distribution, and product mix — so the CRO can assess whether origination quality is improving or deteriorating relative to the portfolio standard.

Daily
Current week

The live CRO dashboard: what assembles in 58 seconds

CRO Risk Dashboard — Karnataka NBFC · Portfolio ₹2,840 Crore
Assembled: Nov 14, 2025 · 08:00:58 · Data current as of: 07:58 · Assembly time: 58 seconds
2.9% Gross NPA ratio ▲ +12bps vs last week · Watch
19.0% CRAR (actual) 15.6% under 200bps stress
2.60% Portfolio NIM 1.48% under 200bps rate shock
84.2% Provision coverage ▼ Below 85% threshold · Alert
NPA distribution by segment — current
Home loan (salaried)
1.8% NPA — within appetite
1.8% ✓
MSME business loan
3.8% NPA — approaching limit (4.5%)
3.8% ⚑
Personal loan (SE)
5.2% NPA — exceeds 4.5% appetite
5.2% ✗
LAP
3.1% NPA — within appetite
3.1% ✓
Gold loan
0.8% NPA — well within appetite
0.8% ✓
Leading indicators — early warning signals
Accounts 60–89 DPD
284 accounts · ₹38.4Cr · Up 18% vs last month
284 ⚑
NACH failure rate (Nov)
3.8% this month · vs 3.1% Oct average
3.8% ⚑
New disbursal avg CIBIL
718 this week · vs 712 portfolio avg · Improving
718 ✓
Active alerts requiring CRO attention
SE personal loan NPA at 5.2% — exceeds 4.5% risk appetite ceiling. Segment origination should be reviewed. 3 large accounts drive 41% of the segment NPA — account-level brief attached.
Provision coverage 84.2% — below 85% monitoring threshold. ₹24Cr additional provisioning required to restore coverage to threshold. Provisioning AI has flagged 18 accounts pending valuation update.
60–89 DPD pool +18% vs last month — 284 accounts approaching NPA threshold. Collection team has contacted 71%. Remaining 29% (83 accounts) are uncontacted — escalation recommended.
CRAR 19.0% — 4.0pp above 15% regulatory floor. Under 200bps stress CRAR is 15.6%. Current buffer is adequate but management action underway (per Q3 stress test).
58sDashboard assembly time — 6 data sources, all live, assembled and formatted in 58 seconds on demand
2Critical alerts today — SE personal loan NPA above appetite and provision coverage below threshold
284Accounts in the 60–89 DPD pool — the forward-looking NPA indicator that predicts next quarter's classification run
ZeroManual steps between data sources and CRO dashboard — direct API pulls, no extract-transform-load

The 60-second dashboard is not a speed achievement — it is a governance requirement

A CRO who can see the current risk position in under a minute, on demand, any day of the week, is a CRO who is equipped to perform their function. A CRO who must wait for a weekly report or request a manual data extraction is structurally impaired in their ability to identify emerging risks before they become material events. The Risk Reporting Agent AI does not speed up the old process — it eliminates the wait by replacing the extraction-transformation-reporting chain with a direct connection between the live operational systems and the decision-maker who needs to see them. The 58 seconds is the connection time. The data has always been there.

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