Supervisory Analytics — Cubot BI
Solution

Supervisory
Analytics

Financial regulators collect enormous volumes of structured data from the institutions they supervise. The real value lies not in receiving it — but in using it. Cubot BI transforms regulatory submissions into a live intelligence layer: monitoring institutional health, running stress scenarios, and surfacing early warning signals before problems become crises.

info@cubotbi.com
Supervisory Dashboard
Live
Bank A — Capital Adequacy
CAR dropped below 10.5% threshold — 3rd consecutive quarter of decline
Critical
Insurer B — Solvency Ratio
Solvency margin at 112% — approaching minimum requirement of 100%
Watch
NBFC C — Liquidity Coverage
LCR trend declining — 6-month moving average below peer median
Monitor
Bank D — NPA Ratio
Gross NPA improved to 3.1% — within acceptable range, trend positive
Clear
Updated on latest submission cycle · 47 entities monitored
Capabilities

From data received to intelligence acted on

Supervisory analytics transforms regulatory submissions into a continuous intelligence layer — enabling regulators to monitor, assess, and act on the financial health of the sector they oversee.

Prudential Monitoring
Continuous tracking of capital adequacy, liquidity ratios, leverage, and asset quality across every supervised entity — with automated breach detection and trend analysis.
Early Warning System
Multi-tier signal detection that flags deteriorating institutions before they breach regulatory thresholds — giving supervisors time to intervene rather than react.
Stress Testing
Scenario-based stress tests applied across the supervised portfolio — modelling the impact of macro shocks, rate changes, and credit deterioration on individual institutions and the sector.
CAELS Assessment
Structured CAELS ratings computed from submitted data — Capital, Assets, Earnings, Liquidity, and Sensitivity tracked per entity and aggregated across sectors and peer groups.
Peer & Sector Benchmarking
Compare any entity against its peer group, industry median, and historical self — identifying outliers, best-in-class performers, and systemic patterns across the supervised universe.
Regulatory Reporting & Statistics
Automated generation of sector-level statistics, monetary surveys, cross-border flow analysis, and management reports — drawn directly from the supervised data pool.
CAELS Framework

Structured supervisory ratings from submitted data

CAELS provides a structured framework for assessing the overall condition of supervised institutions — translating financial data into a composite risk rating across six dimensions. Cubot BI computes CAELS scores automatically from regulatory submissions, tracks them over time, and surfaces institutions whose ratings are deteriorating before they reach a critical threshold.

CAELS ratings are computed automatically from submitted data each reporting cycle — giving supervisors a current, consistent, and comparable view of every institution in the supervised portfolio.

C
Capital Adequacy
CAR, Tier 1 ratio, leverage ratio — tracked against regulatory minimums and peer benchmarks.
A
Asset Quality
NPA ratios, provision coverage, classified asset trends, and concentration risk by sector and borrower.
E
Earnings
Return on assets, net interest margin, cost-to-income ratio — profitability trends over rolling periods.
L
Liquidity
LCR, NSFR, funding concentration, and short-term liquidity gap — static and dynamic measures.
S
Sensitivity
Interest rate risk, market risk exposure, and sensitivity to macro variable changes in the portfolio.
+
Composite Rating
Weighted CAELS score aggregated per entity — drillable, comparable, and tracked over time.
Stress Testing & Early Warning

Engineered rules,
surfaced on screen

The solution is built with pre-configured analytical rules — stress scenarios, threshold logic, and signal detection — all designed for financial supervisory use. Cubot BI surfaces the outputs: ranked results, trend views, and entity-level drill-downs that give supervisors a structured basis for action.

Stress tests model the impact of adverse macro and credit scenarios across the supervised portfolio. The EWS monitors composite signals per entity — flagging deteriorating institutions across three tiers before thresholds are breached.

EWS — Three alert tiers
Tier 1
Immediate Action
Breach imminent or occurring. Formal corrective action plan triggered.
Tier 2
Enhanced Monitoring
Deteriorating trend across multiple indicators. Heightened review frequency.
Tier 3
Watch & Track
Single adverse indicator. Logged and escalates if trend persists.
Stress scenario results — current cycle
Rate Shock +300bps
Impact on NIM and capital across supervised portfolio
−2.4% CAR avg
3 Fail
Credit Deterioration
NPA ratio +3% across lending book
−1.8% CAR avg
6 Watch
Liquidity Stress
30-day outflow shock to LCR
LCR −18pts avg
4 Watch
Macro Downturn
GDP −3%, unemployment +2%
−0.9% CAR avg
All Pass
EWS signals — active
Sustained Capital Erosion — Bank A
CAR declining 3 consecutive periods — breach projected within 2 quarters.
Tier 1
NPA Acceleration — NBFC C
NPA growth rate exceeding peer median by 1.5 standard deviations.
Tier 2
Profitability Compression — Bank D
NIM declining below cost of funds over rolling 4 quarters.
Tier 3
Applicable Bodies

Built for financial supervisors

Cubot BI's supervisory analytics platform is designed for any authority responsible for the ongoing oversight of financial institutions — from central banks and prudential regulators to capital market authorities and sector-specific supervisors.

Central Banks
Banking & Prudential Regulators
Securities & Capital Market Regulators
Insurance & Pension Regulators
Financial Stability Authorities
Micro-prudential Supervisors
Macro-prudential Authorities
AML / Financial Intelligence Units

Discuss your supervisory data needs

Get in touch to explore how the platform can be configured for your supervised portfolio, reporting requirements, and analytical priorities.

info@cubotbi.com
www.cubotbi.com
CUBOT BI for Regulators