Credit Risk Analytics & Reporting

Vector ML Analytics Provides Tools to Assess and Manage This Risk, Helping Banks Mitigate Losses and Comply With CECL and IFRS 9 Standards by Modeling Expected Credit Losses. With Advanced Analytics, Vector Supports Financial Projections for Loan Portfolios, Enabling Data-Driven Decisions and Optimized Credit Risk Strategies.

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Credit Risk Features

360° Reporting and Analytics
Cumulative Static Loss

Cumulative Static Loss

Vector ML Analytics Provides Static and Dynamic Loss Analysis to Help Banks Predict Potential Losses and Assess Credit Risk. Static Loss Analysis Evaluates Historical Data to Establish Baseline Loss Rates, While Dynamic Loss Analysis Uses Predictive Modeling to Simulate Scenarios Like Economic Shifts and Borrower Behavior Changes. This Combined Approach Enables Banks to Assess Risk, Improve Forecasts, Enhance Stress Testing, and Optimize Risk Management Strategies, Equipping Them to Proactively Manage Potential Losses and Maintain Financial Stability.

Delinquency Matrix

Delinquency Matrix

Vector ML Analytics Provides Advanced Delinquency Migration and Roll Rate Matrix Tools, Enabling Banks to Track How Loans Transition Between Delinquency Stages Over Time. This Analysis Identifies Patterns in Delinquency Behavior, Such as Increased or Improved Performance, Allowing Banks to Assess Emerging Risks. Roll Rate Matrices Further Detail the Likelihood of Loans Advancing to Higher Delinquency Stages or Recovering to Lower Stages, Offering a Comprehensive View of Loan Performance Dynamics Essential for Effective Risk Management.

Expected Credit Loss (ECL)

Expected Credit Loss (ECL)

Expected Credit Loss (ECL) Is a Critical Credit Risk Metric Representing the Net Loss When a Borrower Defaults, Calculated as ECL = PD x LGD x EAD. Vector ML Analytics Provides Tools to Model and Manage ECL Accurately, Enhancing Risk Management. Probability of Default (PD) Estimates the Likelihood of Default Using Machine Learning on Borrower Data. Loss Given Default (LGD) Measures the Percentage Lost After Accounting for Recoveries. Exposure at Default (EAD) Represents the Loan's Exposure at Default, Predicted Through Survival Analysis. Together, These Elements Enable Precise ECL Calculations to Support Effective Credit Risk Management.

CECL & IFRS 9

CECL & IFRS 9

Compliance With CECL and IFRS 9 Standards Is Essential for Financial Institutions to Ensure Accurate Estimation of Expected Credit Losses, Promoting Transparency and Stability. Vector ML Analytics Provides Advanced Tools to Model and Report These Losses, Aligning With Regulatory Requirements. CECL Mandates a Forward-Looking Approach Using Historical Data, Current Conditions, and Forecasts, While IFRS 9 Uses a Three-Stage Impairment Model Based on Credit Risk Changes. Vector Supports These Frameworks With Predictive Models, Real-Time Updates, and Scenario Analyses, Enhancing Risk Assessment and Regulatory Compliance.

Credit Risk Analytics & Reporting Demo

Essential Tools for Effective Credit Risk Analytics

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Research and Articles.

Machine Learning
Applications in Lending

As of 2020, the financial sector debt reached $66 trillion equal to 25% of the global debt. As the world gears up to fight the pandemic, the need to precisely analyze credit risk and price...

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Automated
Machine Learning

AutoML is the automation of ML algorithms and the structured design process of a defined model. It provides predesigned systematically structured data analysis tools that help banks...

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Buy Now Pay Later

Buy Now, Pay Later (BNPL) offers consumers the ability to purchase immediately and pay over time, rapidly evolving from simple 'Pay-in-4' plans to diverse financial models and integrated apps. The sector, primarily funded by merchant fees and occasionally featuring interest or late fees, prioritizes market share over immediate profitability. As it faces stiff competition from both new entrants and traditional financial institutions, BNPL companies must innovate to maintain growth.

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What is Credit Risk
and Why is it Important?

The lender offers credit to a debtor, termed a borrower, based on trust in repayment. Credit risk emerges when there's a risk the borrower might not repay, leading to the lender not receiving the principal or interest, thereby incurring significant financial losses. Additionally, substantial costs are often incurred in efforts to recover the loan amount.

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Financial and
Regulatory Reporting

Financial regulations, such as Basel and IFRS 9, dictate capital requirements for institutions to ensure economic stability. Government regulators enforce these to prevent crises like the 2008 financial meltdown, which caused major disruptions due to major institutional bankruptcies. Trust in banks, fostered by these regulations, encourages public deposits, crucial for economic functionality.

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Scorecard - Credit Engine

Credit risk scorecards provide quantitative estimates of the likelihood of customer delinquencies, such as bankruptcy or loan default, in relation to their credit status. We've developed a fast, automated credit risk engine to enhance decision-making efficiency. Upcoming posts will explore common ML models used in the consumer finance credit risk sector.

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Prepayment Analytics

The Loan Portfolio Cash Flow Forecast & Analytics offers in-depth cash flow projections accounting for defaults and prepayments, essential for financial planning and interest risk management. It also supports Investor & Warehouse Securitization Reporting with comprehensive pool analysis and aids in Loan Pricing & Scenario Optimization to maintain a balance between profitability and risk.

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