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Lending Analytics
Software Features, Integrations, Costs, and Benefits
Since 1989 in data analytics, ScienceSoft helps financial institutions build tailored solutions for data-driven credit risk assessment, informed improvement of loan portfolio management, and streamlined loan servicing.
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Lending Analytics: The Essence
Lending analytics helps drive insights from loan, borrower, market, and other credit data. Lending analytics software lets companies make risk-proof lending decisions, get a real-time view of loan portfolio performance and loan servicing, stress-test lending strategies via what-if models, and more.
- Key integrations: loan management system, treasury software, CRM, accounting software, customer-facing apps, credit rating bureau platforms, financial data marketplaces.
- Costs: $100,000–$600,000, depending on software complexity.
- ROI: up to 260% with a payback period of 3 months.
Key Features of Lending Analytics
Underwriting analytics
- Automated segmentation of personal and commercial borrowers (e.g., per monthly income, location, and age for personal loans; per loan type, industry for commercial loans, etc.).
- Estimating borrower creditworthiness through assessment models based on internal policies and credit rating scores provided by credit rating bureaus.
- Borrower’s debt-to-income ratio calculation.
- Recommendations on loan approval or decline.
- Calculating the share of overdue debt in a customer portfolio.
- Consolidated and distributed credit rating calculation for multi-entity borrowers.
- AI-powered detection of connections between loan applicants and current loan recipients.
- AI-powered recommendations on risk-proof loan terms (e.g., assigning personalized credit limits based on each borrower’s risk level).
Collateral analytics
- Non-financial collateral evaluation (e.g., cars, real estate, equipment) based on its condition and market price data.
- Financial collateral evaluation (e.g., shares, stocks).
- Continuous tracking of collateral status, value, and condition.
- What-if scenarios for collateral value with various risk factors, including market volatility and potential value obsolescence.
Lending team performance analytics
- Tracking loan origination metrics like average cycle time, average loan amount, application approval rate.
- Monitoring loan servicing team performance metrics (e.g., the number of applications processed, profit per employee, application-to-funding ratio).
- Real-time monitoring of debt collector performance (e.g., number of applications processed by a collector, profit per collector).
Loan servicing analytics
- Real-time monitoring of loan repayment status.
- Automated identification of delinquent payments with notifications to loan officers.
- Analyzing borrowers’ repayment history (e.g., missed/on-time/late payments, early loan repayment, payment frequency and amounts).
- Continuous monitoring of credit score changes and how they can influence the borrower’s repayment ability.
- AI-powered debtor-specific collection strategies based on the analysis of historical data on borrower interactions and payment behavior.
- Real-time monitoring of debt recovery progress (e.g., debtor response rate, amount recovered by period).
- ML/AI-assisted delinquency rate and cash flow forecasting depending on collection performance.
- AI-powered suggestions on debt collection prioritization (e.g., based on debt aging, loan amount).
Loan portfolio performance analytics
- Tracking portfolio profitability metrics (e.g., profit per loan, total financial gains per portfolio).
- Portfolio performance benchmarking against pre-defined parameters.
- ML/AI-assisted forecasts of loan demand and profitability based on historical transaction data, borrower behavior patterns, and financial market data analysis.
- AI-powered recommendations on optimal loan portfolio limits (e.g., by loan type, transaction volume).
Loan portfolio risk analytics
- Monitoring of portfolio risks like non-performing loans (NPL), loss given default (LGD), exposure at default (EAD), the probability of default (PD).
- Assessment of loan-specific risk factors like currency exchange rates and geopolitical risks for cross-border loans; loan-to-value ratios and property appraisals for mortgage loans through ML/AI-powered forecasts and what-if models.
- Monitoring factors influencing portfolio performance (e.g., delinquency and default rates, portfolio diversification) across all types of loans, including commercial, personal, mortgage, and syndicated loans.
- Real-time portfolio transaction tracking, including disbursements and repayments.
- AI-powered suggestions on risk-mitigation steps (e.g., identifying optimal FX rate exposures for multi-currency loans or maximum exposure limits for loan portfolio risks).
Loan pricing analytics
- Automated calculation of loan-specific prices based on the chosen pricing strategy and risk appetite.
- Calculating the optimal price for new loan offerings, e.g., based on competitor prices.
- Building and stress-testing what-if scenarios of loan profitability pricing strategies under various factors like interest rate, credit risk, liquidity, currency exchange rates.
- Loan price optimization (e.g., suggestions on lower interest rates for borrowers with higher credit scores).
- ML/AI-powered what-if scenarios for the profitability of different loan pricing strategies.
- Tracking financial performance metrics (e.g., AR turnover ratio, operating cash flow).
- Calculating debt share in a portfolio and debt-to-share ratio.
- Automated revenue attribution to certain loans with insights into their profitability.
- Analyzing tax implications of loans (e.g., insights into tax deductibility of interest payment, assessing bad debt impact).
- Tracking transactions related to multi-currency loans (e.g., alerts on unfavorable exchange rate fluctuations, generation of multi-currency reports).
- ML/AI-powered financial performance forecasting and scenario modeling (e.g., projected cash flow, revenue per loan under the chosen interest rate).
Fraud analytics
- Identifying suspicious patterns in loan applications, e.g., inconsistencies in personal information, multiple loan applications for the same individual.
- Detecting abnormal borrower behavior and transaction patterns (e.g., money transfer to an unknown account).
- ML/AI-powered identification of fraudulent loan applications, forged documents, and fly-by-night companies.
- Fraud detection alerts.
Essential Integrations for a Lending Analytics Solution
Note: we can also integrate your software with case-specific solutions, e.g., a core banking system, mortgage automation software, P2P lending software.
- To provide a 360-degree view of loan portfolio performance and enable its informed improvement.
- To assist in borrower risk mitigation.
- To monitor regulatory compliance adherence.
- To ensure efficient debt collection.
- To enable informed optimization of hedging strategies.
- To manage collateral risks and optimize loan-to-value ratios.
- To enable multidimensional customer segmentation and tailor credit plan offerings.
- To evaluate customer satisfaction level.
- To optimize revenue generation and cash flow management
- To enable accurate revenue and expenses recording in the general ledger.
Customer-facing apps
E.g., a customer portal, customer mobile apps, a company website.
- To identify patterns in borrower repayment behavior.
- To detect loan application fraud.
Bank accounts or payment software
- To enable tracking of loan-related transactions and streamlined loan reconciliation.
- To identify transactional fraud.
Credit rating bureau platforms
E.g., Experian, Equifax.
- To simplify credit rating checks.
- To enable automated credit reports submission.
Financial data marketplaces
- To get real-time visibility into the market situation.
- To build what-if scenarios of market-related impact.
- To enable collateral analytics.
Things to Consider When Building Lending Analytics Software
Tailored risk assessment models
Identifying risk assessment factors specific to loan types and customer groups you deal with is essential. For instance, if you grant mortgage loans, you will likely benefit from loan-to-value ratio and property type analytics. If you work with personal loans, you may need to prioritize evaluating income stability and existing debt obligations.
Custom reports and dashboards
Analytics software needs to produce dashboards that can be easily tailored to different user groups. For example, underwrites may leverage drill-down capabilities for comprehensive borrower risk assessment, while debt collection teams may need real-time updates to prioritize their efforts.
Data security
Analytics software should support the encryption of personal and financial data at rest and in transit to prevent data loss and damage. Among the necessary security features is also role-based access management. It will help you ensure your employees don’t get information outside their responsibilities and permissions.
Regulatory compliance aspects
Analytics software must enable data storage and transfer according to personal data protection laws like SOX, SOC1 and SOC2, PCI DSS, GDPR, NYDFS, CCPA. Additionally, it can facilitate compliance with financial management and customer protection regulations like TILA, RESPA, HDMA, FCRA, ECOA for the US, MCD for the EU, SAMA for the KSA. One of the key features to facilitate compliance is the generation of standardized documents for reporting to authorities (e.g., Metro 2 form).
Highlights of ScienceSoft’s Lending Analytics Portfolio
Cost & ROI of Lending Analytics Software
Lending analytics software implementation may cost from $100,000 to $600,000+, depending on software complexity. Some cost-defining factors are the number of integrated sources, data processing specifics (batch or real-time), the use of ML/AI-powered capabilities, and more.
On average, data analytics in lending brings an ROI of up to 260%, breaking the investment even within 3 months. The main ROI drivers are capabilities for what-if scenarios creation and comprehensive credit risk assessment.
$100,000–$150,000
A basic solution that ensures:
- KPI tracking across 1–2 analytics areas, e.g., loan servicing metrics.
- Integration with 1–2 key data sources, e.g., a loan management system.
- Batch data processing (e.g., every 12 hours).
- Scheduled and ad hoc reporting.
$150,000–$400,000
A solution of medium complexity that ensures:
- KPI tracking across multiple business areas, e.g., finance, loan management, etc.
- Integration with 3–7 data sources, including corporate software and external systems.
- Batch and real-time data processing.
- Diagnostic and predictive analytics via ML models.
- Automated reporting to regulatory authorities.
$400,000–$600,000+
An advanced solution that ensures:
- Comprehensive analytics across all the required business areas, including market situation.
- Integrating multiple internal and third-party systems, including software powered by blockchain.
- Real-time big data analytics (e.g., for transactional fraud detection).
- Advanced ML-powered route-cause analysis and forecasting.
- AI-powered recommendations on process optimization and service personalization.
- Automated generation of consolidated reports and reports compliant with local regulations.
Benefits of Lending Analytics
ScienceSoft is Ready to Assist You at Every Step of Lending Analytics Implementation
Our team can create a robust analytics solution to consolidate multi-source data you need for comprehensive lending analysis. We’ll build tailored features based on the specific factors considered in your business, including the markets you operate on and customer segments you target.
Consulting on lending analytics
You can request consulting on end-to-end solution implementation (e.g., with feasibility study delivery, architecture design, and tech stack selection) or get professional recommendations on individual components (e.g., loan-specific what-if models, regulatory compliance framework).
Implementation of lending analytics
We can build a scalable, fault-tolerant solution for analytics-based lending. We tailor software capabilities to the specifics of the loans you work with and build user-centric dashboards (e.g., for underwriters, debt collectors). We implement custom data management mechanisms to ensure complete regulatory compliance and sensitive data protection.
About ScienceSoft
ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. Since 1989, we build industry-specific analytics solutions for 30+ domains and help businesses get a data-driven view of their performance, adopt efficient risk mitigation, and get valuable insights for making day-to-day and strategic decisions. Being ISO 9001 and ISO 27001-certified, we can guarantee top software quality and complete security of our customers’ data.