Lending Analytics — CUBOT BI
Solution

Lending &
Credit Analytics

Loans and credit are at the core of financial operations. Improving underwriting, sales, and repayment follow-up requires more than good data — it requires a platform that can process it at scale, compute meaningful metrics, and surface predictive insights where decisions are made.

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NPA
Non-Performing Assets
PD
Probability of Default
LGD
Loss Given Default
PCR
Provision Coverage Ratio
Key credit metrics computed automatically, surfaced in real time across the portfolio.
Capabilities

Across the lending lifecycle

From origination through collections, the platform handles large volumes of customer loan data — computing scores, tracking performance, and flagging risk — so teams can focus on decisions, not data preparation.

Underwriting Analytics
Score and profile applicants at volume. Segment by risk category, track approval rates, and monitor the quality of what enters the book against policy thresholds.
Repayment Tracking
Monitor repayment behaviour across the portfolio. Days-past-due trends, missed payment patterns, and early warning signals surface before accounts deteriorate into NPA.
Default Probability
Predictive models compute the probability of default at the customer and portfolio level. Outputs are updated on schedule and available for review and action in the dashboard.
Provision & NPA Reporting
NPA ratios, provision levels, and coverage statistics computed automatically from loan data. Drill from portfolio summary to individual account without rebuilding reports.
Portfolio Segmentation
Slice the book by product type, geography, borrower segment, vintage, or risk grade. Compare performance across segments and track how the mix evolves over time.
Collections Intelligence
Prioritise collections effort using behavioural data. Identify which accounts are most responsive, track recovery rates, and measure campaign effectiveness over time.
Credit Metrics

What gets measured

The platform computes a comprehensive set of credit and portfolio metrics from raw loan data. These are available as standard outputs, configurable by period, segment, and product — and drillable to account level.

Asset Quality
NPA Ratio
Non-performing assets as a share of total advances, by segment and in aggregate.
Provisioning
Provision Coverage
Provisions held against classified assets. PCR tracked over time and against benchmarks.
Predictive
Default Probability
PD scores computed at customer and portfolio level on a rolling basis.
Predictive
Loss Given Default
Expected loss severity on defaulted accounts based on collateral and historical recovery.
Repayment
Days Past Due
DPD buckets tracked across the book. Early-stage delinquency trends surface risk before classification.
Repayment
Collection Rate
Repayment as a percentage of due amounts, by product, region, and collection channel.
Portfolio
Vintage Analysis
Performance of loan cohorts by origination period. Identifies deterioration patterns early.
Portfolio
Concentration Risk
Exposure by sector, geography, and borrower group. Tracks against concentration limits.
Data Handling

Built for national-scale portfolios

Financial institutions operating at national scale generate loan data across millions of customers, multiple products, and dozens of geographies. The platform is built to handle that volume — processing customer-level data without aggregating away the detail that matters.

Whether the portfolio is 50,000 accounts or 50 million, the data model handles individual loan records with full history — enabling both high-level portfolio views and account-level drill-down in the same platform.

Data Capability
Customer-level records
National
Transaction history
Full depth
Portfolio segmentation
Multi-dim
Predictive scoring
Per account
Real-time refresh
Scheduled
Designed to process large-volume, customer-wise loan data — from individual account detail up to national portfolio aggregates — without losing the granularity that powers meaningful analytics.
How It Works

From loan data to insight

01
Connect Source Data
Loan management systems, core banking, and ERP data connected and staged.
02
Build the Data Model
Customer-level loan records structured into a clean, queryable data model automatically.
03
Compute Metrics
NPA, PD, provisions, DPD, vintage performance — calculated and stored on schedule.
04
Deliver on Screen
Dashboards, drill-downs, and alerts surfaced where decisions are made.