Financial Insights and Analytics

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Overview

FinPro’s Financial Insights and Analytics engine processes bank statements, transaction data, and financial information to extract meaningful patterns, calculate key metrics, and flag risk indicators. This capability enables Financial Information Users to make informed credit decisions, detect fraud, and personalize product offerings based on comprehensive financial behavior analysis.

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Core Features

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Transaction Categorization

The analytics engine automatically categorizes all incoming and outgoing transactions across 30+ predefined categories, enabling structured analysis of customer financial behavior.

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Supported Categories

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Cumulative Analysis

The analytics engine consolidates financial data from multiple bank accounts to provide cross-institution insights that single-account analysis cannot deliver.

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Key Cumulative Metrics

Average End-of-Day (EOD) Balance Calculates the average closing balance across all linked accounts to assess overall liquidity and financial stability. Salary Trends Identifies salary credit patterns across accounts, detecting:

• Primary and secondary income sources
• Salary consistency and growth trends
• Irregular or missing salary payments

Aggregate Cash Flow Computes total monthly inflows and outflows across all accounts, providing a complete picture of financial health. Multi-Bank Spending Patterns Analyzes combined spending across categories to identify lifestyle indicators and financial priorities.

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Recurring Pattern Detection

The system uses advanced pattern recognition algorithms to identify recurring credits and debits, which are critical indicators for income stability and spending habits.

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Recurring Credits

• Salary Detection: Flags regular salary credits with metadata including employer name, credit date, and amount consistency
• Rental Income: Identifies periodic rental receipts for property owners
• Investment Returns: Detects recurring SIP returns, dividend credits, and interest payments

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Recurring Debits

• EMI Detection: Identifies loan repayments with 97% accuracy, including loan type inference (home, auto, personal)
• Subscription Services: Flags recurring charges for streaming, software, utilities, and memberships
• Insurance Premiums: Detects periodic insurance payment patterns

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Salary and EMI Detection

FinPro’s analytics engine employs machine learning models trained on millions of transactions to achieve market-leading accuracy in detecting salary credits and EMI debits.

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Salary Detection (97% Accuracy)

The system identifies salary transactions by analyzing:

• Transaction descriptions: Pattern matching for keywords like “SAL”, “SALARY”, “PAYROLL”, “NEFT SAL”
• Amount consistency: Tracking regular credit amounts within expected ranges
• Frequency patterns: Monthly or bi-weekly credit cycles
• Source metadata: Corporate account numbers and employer identifiers

Output Includes:

• Employer name (when identifiable)
• Monthly salary amount
• Credit date and frequency
• Salary consistency score
• Detected irregularities (late payments, amount changes)

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EMI Detection (97% Accuracy)

The EMI detection algorithm identifies loan repayments by evaluating:

• Transaction descriptions: Keywords like “EMI”, “LOAN”, “INSTL”, “INSTALLMENT”
• Amount stability: Fixed or decreasing EMI patterns
• Lender identification: Bank or NBFC identifiers
• Repayment schedule: Monthly debit cycles

Output Includes:

• Lender name
• EMI amount and frequency
• Estimated outstanding tenure
• Loan type inference (home, auto, personal, business)
• Repayment consistency and delays

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Custom Business Rules Engine

FinPro allows Financial Information Users to define proprietary underwriting logic and decision rules tailored to their specific use cases.

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Rule Definition Capabilities

• Conditional Logic: Build if-then-else rules based on transaction data, balances, and calculated metrics
• Mathematical Operations: Compute FOIR (Fixed Obligation to Income Ratio), DBR (Debt Burden Ratio), and other custom ratios
• Thresholds and Flags: Set limits for risk indicators such as bounce frequency, cash withdrawal patterns, and minimum balance violations
• Multi-Parameter Rules: Combine multiple conditions across categories (e.g., high alcohol spend + frequent gambling + low savings)

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Use Case Examples

Credit Risk Scoring Create rules that assign credit scores based on:

• Salary consistency (regular vs irregular income)
• EMI burden as a percentage of income
• Savings rate (monthly surplus after expenses)
• High-risk transaction frequency

Fraud Detection Flag suspicious patterns such as:

• Multiple small transactions followed by large withdrawals
• Sudden spikes in cash deposits
• Transactions on bank holidays
• Round-figure tax payments without corresponding income

Product Personalization Offer targeted products based on:

• Investment activity (mutual fund buyers)
• Loan eligibility (low EMI burden, high salary)
• Insurance prospects (healthcare spending patterns)
• Premium services (high account balance, frequent travel)

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Integration Options

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Data Sources

FinPro’s analytics engine can process financial data from multiple sources:

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API Access

Analytics results are accessible via RESTful APIs that return structured JSON responses with categorized transactions, computed metrics, and risk flags. Key API Endpoints:

• /fi/data - Retrieve categorized transaction data
• /fi/balance - Access computed balance metrics
• /analytics/summary - Get aggregated financial insights
• /analytics/risk-indicators - Fetch irregularity and risk flags

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Irregularity and Risk Indicators

In addition to transaction-level analytics, the system detects suspicious patterns and risk flags that may indicate financial stress, fraud, or non-compliance.

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Detected Risk Indicators

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Use Cases for Risk Indicators

Enhanced Fraud Detection Identify applicants engaging in income inflation, statement tampering, or circular transaction schemes. Deeper Risk Profiling Assess financial stress indicators such as frequent bounces, high cash dependency, and irregular income patterns. Sharper Underwriting Make informed lending decisions by combining traditional credit bureau data with behavioral risk flags derived from transaction analysis. Regulatory Compliance Flag potentially suspicious transactions that may require reporting under AML/CFT regulations.

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Outcomes and Benefits

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Faster Turnaround Time

Automated categorization and analytics reduce manual review effort, enabling faster credit decisions and customer onboarding.

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Unified Data Analytics

Consolidate insights from multiple data formats (AA, PDF, GST, CIBIL) into a single analytical framework for consistent decision-making.

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Enhanced Underwriting Capability

Leverage alternate data sources and behavioral insights to expand credit access while maintaining risk discipline.

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Improved Monitoring

Continuous analysis of recurring consents enables proactive risk management for loan portfolios and collections optimization.

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Personalized Experiences

Use financial behavior insights to tailor product recommendations, interest rates, and credit limits to individual customer profiles.

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Getting Started with Analytics

API Reference

Explore Data Management and Analytics APIs

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Support

For questions about analytics configuration, custom rule creation, or integration support, contact the Moneyone technical team through the FinPro admin portal.