Course Introduction
The Credit Analytics and Lending Intelligence Training Course provides a practical framework for transforming lending and credit data into actionable intelligence for underwriting, portfolio management, credit risk monitoring, collections, customer analysis, and lending strategy. The program combines credit-domain concepts with modern data analytics techniques to help participants understand borrower behavior, loan performance, portfolio quality, repayment patterns, exposure concentrations, and emerging credit risks through structured and evidence-based analysis.
Participants will learn how to work with diverse credit datasets, including borrower profiles, applications, loans, repayment schedules, balances, interest, collateral, credit scores, delinquency records, restructurings, write-offs, recoveries, collections activities, income information, and transaction histories. The course emphasizes data quality, consistent borrower and facility identifiers, loan-level analytical structures, time-based analysis, segmentation, exposure measurement, and portfolio-level aggregation so that lending organizations can establish reliable foundations for credit intelligence.
A major component of the course focuses on credit portfolio analytics. Participants will examine loan origination volumes, approval rates, utilization, outstanding balances, delinquency, arrears, non-performing exposures, recoveries, write-offs, portfolio concentration, repayment behavior, and product-level performance. They will learn how to compare portfolio outcomes across borrower segments, industries, geographic markets, loan products, risk grades, branches, relationship managers, and origination periods to identify important patterns and changes in credit quality.
The course also develops practical underwriting and borrower intelligence capabilities. Participants will learn how to analyze application characteristics, borrower financial information, historical repayment behavior, debt-service indicators, collateral information, credit characteristics, and lending outcomes. Analytical techniques will be used to support credit decision processes, application segmentation, portfolio monitoring, and early identification of potentially deteriorating accounts, while recognizing that analytical models and indicators should complement appropriate credit policies, governance, and professional review.
Predictive analytics forms an important part of the program, with emphasis on classification, probability-based scoring, delinquency prediction, default-risk modeling, loss forecasting, collection prioritization, and portfolio stress analysis. Participants will explore how historical lending data can be prepared for predictive modeling, how model performance can be evaluated, and how analytical outputs can be interpreted responsibly. The course also addresses model validation, explainability, data quality, monitoring, fairness, and governance considerations relevant to credit analytics.
Modern business intelligence platforms provide practical capabilities for implementing these analytical workflows. Power BI can connect to many data sources, use Power Query for repeatable data preparation, create semantic models with relationships and calculations, and build interactive reports and dashboards. Power BI also supports scheduled refresh, alerts, sharing, security roles, and other service capabilities that can support governed lending analytics environments.
By the end of the five-day program, participants will be able to develop an integrated credit and lending intelligence framework covering borrower analytics, underwriting performance, portfolio quality, delinquency, collections, credit exposure, concentration, profitability, predictive risk indicators, and executive reporting. Through practical exercises and a capstone project, learners will analyze realistic lending datasets, develop credit KPIs, investigate portfolio trends, build an interactive lending intelligence dashboard, and present evidence-based findings for credit managers, risk professionals, lenders, financial analysts, and senior management.
Duration
5 days
Who Should Attend
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Credit analysts responsible for evaluating borrower, facility, repayment, and portfolio information.
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Credit risk professionals monitoring portfolio quality, delinquency, concentration, exposure, and emerging risk indicators.
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Lending managers seeking stronger analytical capabilities for loan origination, portfolio management, and credit performance.
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Underwriting professionals interested in using data analytics to strengthen application and borrower assessment processes.
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Portfolio managers responsible for monitoring loan performance across products, sectors, geographies, and borrower segments.
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Collections and recovery professionals analyzing arrears, delinquency, repayment behavior, recoveries, and collection performance.
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Banking data analysts working with borrower, loan, transaction, repayment, collateral, and credit-risk datasets.
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Financial analysts supporting lending profitability, interest income, credit costs, portfolio performance, and management reporting.
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Risk managers developing analytical frameworks for credit exposure, portfolio concentration, early-warning indicators, and stress analysis.
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Loan officers and relationship managers seeking practical methods for using customer and lending data in portfolio management.
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Business intelligence professionals building credit dashboards, analytical models, KPIs, and executive lending reports.
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Banking and financial-services executives seeking practical capabilities for data-driven lending and credit decision support.
Course Objectives
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Develop a comprehensive understanding of credit analytics and lending intelligence and apply analytical techniques to borrower, facility, repayment, collateral, transaction, and portfolio data.
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Build reliable credit datasets by integrating applications, borrower profiles, loan accounts, repayment transactions, balances, delinquency records, collateral information, and collections activity.
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Calculate and interpret credit portfolio indicators including approval rates, utilization, outstanding exposure, delinquency, non-performing exposures, recoveries, write-offs, and repayment performance.
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Analyze borrower and loan segments across products, risk grades, industries, geographies, branches, origination periods, and customer characteristics to identify changes in credit performance.
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Develop analytical approaches for underwriting performance by examining application characteristics, approval outcomes, borrower attributes, credit grades, facility structures, and subsequent repayment behavior.
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Analyze delinquency and collections data to identify arrears patterns, aging concentrations, repayment behavior, collection effectiveness, recovery performance, and accounts requiring additional attention.
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Apply predictive analytics concepts to default-risk estimation, delinquency prediction, collection prioritization, loss forecasting, borrower segmentation, and early-warning credit intelligence.
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Analyze portfolio concentration and credit exposure across sectors, geographies, products, borrower groups, collateral types, risk grades, and other dimensions relevant to portfolio management.
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Design interactive credit intelligence dashboards that communicate portfolio quality, lending activity, risk indicators, delinquency, collections, exposure, profitability, and emerging trends.
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Complete an integrated lending analytics project involving data preparation, credit KPIs, portfolio analysis, borrower segmentation, risk indicators, predictive analytics concepts, dashboard development, and management recommendations.
Comprehensive Course Outline
Module 1: Credit Analytics and Lending Intelligence Foundations
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Understanding the role of credit analytics across loan origination, underwriting, portfolio management, credit risk, collections, recovery, and lending strategy.
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Identifying major credit data sources including applications, borrower profiles, loan accounts, repayment schedules, transactions, credit bureau information, collateral, collections, and recovery records.
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Designing lending analytical structures using borrower IDs, facility IDs, product codes, risk grades, branch dimensions, industry classifications, geographic attributes, and reporting periods.
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Establishing credit-data quality principles covering completeness, accuracy, consistency, uniqueness, timeliness, reconciliation, lineage, controlled definitions, and appropriate access management.
Module 2: Credit Data Preparation and Analytical Data Modeling
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Preparing credit datasets by standardizing borrower identifiers, loan numbers, dates, loan statuses, repayment transactions, product classifications, risk grades, and delinquency categories.
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Integrating application, customer, loan, repayment, collateral, transaction, collections, and financial datasets into a consistent analytical environment.
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Designing analytical data models that connect borrowers, facilities, products, transactions, balances, repayment schedules, dates, risk classifications, collateral, and collection events.
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Using Power Query to connect, clean, reshape, and combine lending datasets through repeatable transformation workflows that can be refreshed as source information changes.
Module 3: Lending Origination and Underwriting Analytics
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Analyzing loan application volumes, approval rates, decline rates, processing times, application channels, product demand, and borrower characteristics across lending portfolios.
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Evaluating relationships between application characteristics, borrower attributes, credit grades, loan structures, collateral, approval decisions, and subsequent repayment outcomes.
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Developing underwriting performance measures that compare origination cohorts, products, branches, channels, relationship teams, borrower segments, and risk categories.
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Building analytical views that help credit teams investigate application patterns, approval outcomes, portfolio quality, and changes in lending composition over time.
Module 4: Credit Portfolio Performance and Risk Analytics
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Measuring portfolio balances, outstanding exposure, utilization, repayment performance, delinquency, non-performing exposures, restructurings, write-offs, and recoveries across lending portfolios.
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Analyzing portfolio performance by loan product, borrower segment, industry, geography, branch, risk grade, collateral type, origination cohort, and other relevant dimensions.
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Identifying portfolio concentration patterns by borrower, sector, geography, product, risk category, or other exposure dimensions that may require enhanced monitoring.
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Developing credit portfolio dashboards that combine lending growth, exposure, asset quality, delinquency, loss indicators, and portfolio composition into an integrated management view.
Module 5: Delinquency, Collections and Recovery Analytics
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Analyzing delinquency using days-past-due measures, aging buckets, arrears balances, missed payments, cure behavior, restructuring events, and movement between delinquency stages.
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Measuring collections performance using contact activity, promises to pay, repayment outcomes, collection rates, recovery amounts, account resolution, and collection costs.
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Identifying patterns associated with worsening delinquency, repeated missed payments, extended arrears, restructuring, write-off, and recovery outcomes.
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Developing collections intelligence dashboards that prioritize portfolio visibility across delinquency stages, borrower segments, collection channels, geographic areas, and recovery outcomes.
Module 6: Borrower, Customer and Credit Behavior Analytics
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Segmenting borrowers using income characteristics, credit history, repayment behavior, product relationships, loan utilization, transaction activity, risk classifications, and lifecycle characteristics.
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Analyzing borrower behavior over time to identify changes in balances, payment regularity, borrowing activity, utilization, delinquency, and engagement with lending products.
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Comparing borrower outcomes across customer segments, industries, geographic markets, loan products, risk grades, and origination cohorts to identify meaningful performance differences.
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Developing borrower intelligence dashboards that connect customer characteristics, facility information, repayment behavior, exposure, risk indicators, and lending outcomes.
Module 7: Credit Scoring, Predictive Analytics and Early-Warning Intelligence
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Understanding classification and probability-based modeling concepts used for default-risk estimation, delinquency prediction, application assessment, and credit portfolio monitoring.
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Preparing historical lending data for predictive modeling by defining target outcomes, selecting relevant features, handling missing information, separating development and validation datasets, and evaluating model performance.
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Developing early-warning indicators from repayment behavior, utilization changes, account activity, delinquency movements, exposure changes, and other portfolio signals that may indicate changing credit conditions.
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Interpreting predictive outputs using appropriate validation, explainability, monitoring, governance, and human-review processes rather than treating model scores as automatic lending decisions.
Module 8: Credit Exposure, Profitability and Portfolio Stress Analytics
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Analyzing credit exposure across borrower groups, industries, geographies, products, risk grades, collateral categories, and other dimensions used in portfolio risk management.
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Connecting lending income, interest, fees, balances, operating costs, credit losses, recoveries, and other financial measures to understand lending relationship economics.
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Developing portfolio stress scenarios that examine the potential effects of changes in delinquency, default rates, recovery rates, interest conditions, borrower segments, or economic assumptions.
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Building analytical reports that compare baseline and alternative portfolio scenarios while clearly separating historical results, analytical assumptions, modeled outcomes, and management considerations.
Module 9: Credit Intelligence Dashboards, KPIs and Management Reporting
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Designing credit KPI frameworks covering loan growth, applications, approvals, utilization, exposure, delinquency, non-performing loans, recoveries, write-offs, collections, profitability, and portfolio concentration.
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Building Power BI semantic models, relationships, measures, and calculations to support repeatable lending analytics and consistent credit-performance reporting.
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Creating interactive credit reports with filters, drill-through analysis, trend views, segmentation, exception reporting, geographic analysis, portfolio comparisons, and borrower-level analytical pathways.
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Developing governed lending dashboards and reporting processes using refresh schedules, alerts, sharing, security roles, and appropriate access controls for different credit and risk stakeholder groups.
Module 10: Capstone Credit Analytics and Lending Intelligence Project
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Preparing and validating a realistic lending dataset containing borrowers, applications, loans, repayments, balances, delinquency, collateral, risk grades, collections, recoveries, and portfolio attributes.
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Developing an integrated lending intelligence dashboard presenting origination performance, portfolio exposure, credit quality, delinquency, collections, risk indicators, profitability, and concentration measures.
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Conducting a detailed credit portfolio investigation using segmentation, trend analysis, borrower behavior, delinquency patterns, exposure analysis, early-warning indicators, and predictive analytics concepts.
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Presenting a final credit analytics report that combines data-quality findings, portfolio intelligence, analytical insights, dashboard outputs, risk considerations, and an actionable lending-management improvement roadmap.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
