Lenders hold more data than ever, yet many still approve loans on judgement and a few ratios while delinquency creeps up unnoticed. Over five days, credit professionals learn to turn repayment histories, application data and portfolio records into measurable risk. You prepare and explore lending datasets, engineer predictive variables, develop and validate application scorecards using logistic regression, and interpret gini, KS and ROC results. Further modules cover default likelihood, loss severity and exposure estimation, IFRS 9 expected credit loss staging, vintage and roll-rate analysis, and early warning indicators.
Labs run in Excel, SQL and Python on anonymised loan books, including SME, retail and microfinance examples. The programme suits credit analysts, credit risk and portfolio managers, retail and SME lending heads, microfinance and SACCO managers, model validators, internal auditors and fintech lenders. It is delivered in the classroom, online or in-house with a CPD-accredited certificate. Afterwards you can build a simple scorecard, challenge the assumptions of a vendor model and report portfolio quality with confidence.
Credit losses are the largest threat to bank and non-bank lender profitability, and rising competition from digital lenders has made speed and accuracy in credit decisions a commercial necessity. Supervisors and auditors expect forward-looking provisioning and documented, validated models. Institutions that cannot explain what drives their defaults struggle to price risk or to grow their loan books safely.
Data and Analytics for Credit Risk & Lending Training Course teaches the full analytical chain behind modern lending. Participants start with data quality, definitions of default and sampling, then explore variables and build credit scorecards, from binning and weight of evidence to model fitting and validation. The course explains how probability of default, loss given default and exposure at default combine into expected credit loss under IFRS 9, and how stage allocation and forward-looking information affect provisions. Portfolio analytics follow, covering concentration, migration matrices, vintage curves, collections performance and early warning systems.
Delivery is based on practice. Participants run each technique on sample loan data, interpret outputs critically, discuss model risk and fairness, and prepare a short portfolio quality report suitable for a credit committee.
By the end of the course, participants will be able to:
Participants leave with:
The course follows the model-building process in the order lenders actually use it. Participants work through:
Day 1: Lending Data and Credit Risk Concepts
Day 2: Building Application Scorecards
Day 3: Validation, Stability and Model Risk
Day 4: PD, LGD, EAD and Expected Credit Loss
Day 5: Portfolio Analytics and Early Warning
The programme is designed for professionals who make, model or oversee lending decisions, including:
Participants who attend all sessions and finish the lab exercises are awarded a CPD-accredited Certificate of Completion by Vision Reach Global Consultancy.
Upcoming cohorts
CPD-Accredited
Official invoice & confirmation letter provided
Team discount for 3+ seats
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Everything you need to know about this course before you register.
By the end of the Data and Analytics for Credit Risk & Lending programme, you'll be able to prepare and validate lending data and define default consistently, engineer and select predictive variables for credit models, develop and validate application scorecards using logistic regression, and interpret discrimination and stability measures such as gini, ks and psi. The full breakdown of topics is covered session by session in the Course Outline tab above.
The programme is designed for professionals who make, model or oversee lending decisions, including: Credit analysts and underwriters, Credit risk managers and portfolio managers, Retail, SME and corporate lending heads, Microfinance institution and SACCO managers, Model developers and model validators, Internal auditors and risk compliance officers, Fintech and digital lending product teams, and Finance staff responsible for loan loss provisioning.
Data and Analytics for Credit Risk & Lending Training Course typically runs as 5 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Data and Analytics for Credit Risk & Lending Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Data and Analytics for Credit Risk & Lending starts October 26, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Data and Analytics for Credit Risk & Lending Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Data and Analytics for Credit Risk & Lending Training Course is pitched at intermediate professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Data and Analytics for Credit Risk & Lending Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Data and Analytics for Credit Risk & Lending Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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