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Banking on Predictive Analytics
comprehensive 5-day intensive in-house data mining and predictive analytics course for bankers
“The Basel II Accord, advised
banks to use advanced analytics
techniques to measure, monitor,
manage and mitigate risk levels.
Complying with the Basel II
Accord is a critical issue for global
banking institutions!!”
www.valuefronteira.com
4th Floor (rear wing); 42, Olowu Street, Ikeja
Banking on Predictive Analytics
Introduction
When banks are - on a sustainable basis - able to successfully mine the data that have been
locked up in their operational and financial systems and speedily act upon the emerging
insights, they unequivocally beat competition in such areas as risk management, operational
efficiency, marketing and customer management as well as overall performance optimization.
Banking on predictive analytics enables banks to have mastery over all the potential and actual
key risk exposure areas namely credit, operational, market, financial and more. It equally
enables the banks to gain a single view of customers in such a way as to help increase wallet
share, improve customer satisfaction and loyalty, service mass market customers more cost
effectively and offer the products and services they need. At the level of operations, it helps to
shore up internal operations—including finance, IT, customer service, HR, sales—to ensure the
entire organization is running at peak efficiency.
Course Description
Banking on Predictive Analytics is designed as a comprehensive 5-day intensive and hands-on
data mining and predictive analytics course to equip participants with the skills for analyticsdriven value optimization in their banks along four critical areas namely: credit risk management,
fraud prevention, marketing and operational efficiency. In this regards, participants will be
equipped with the state-of-the-art credit risk measurement and management techniques as
well as the skills for unearthing the needed insights for truly implementing a “one-to-one”
approach to customer relationships. Participants will also learn how to anticipate and quickly
detect fraud and take immediate action to minimize costs.
This course is NOT DESIGNED as open course but to be offered in-house or among a collection of
participants from a maximum of three organizations operating within the financial services
industry. The objective is to build a reasonable cluster of analytics-savvy employees who can
revolutionize business optimization and overall decision making in their firms in favour of facts
and evidence (as against hunches and intuition) based prediction. The overarching expectation
is that the trained clusters of bankers should be able to trigger and sustain a culture of
aggressive search for insights based on evidence backed predictions, proactive as against
reactive responses to issues, all of which are equally accomplished in real time.
Learning Outcomes
At the end of the five-day period, participants will have acquired the skills for:
• Predicting and proactively measuring, monitoring and mitigating credit and operational risk
exposures and defaults using different quantitative techniques;
• Developing analytical models that can guide loan-related decisions that will effectively
minimize potential losses arising from bad loans;
Identifying key markets as well as customer segments, assess business needs and come up
with effective business strategies using powerful algorithms;
Harnessing the capabilities of advanced techniques in analyzing customer-centric data that
helps banks to identify avenues to create business value, thereby making the business
profitable;
Structuring campaigns with higher success rates as well as fine-tune the campaign
management strategy based on insights gathered by monitoring campaign execution and
the aftermaths;
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Banking on Predictive Analytics
Reducing marketing efforts to unlikely buyers. Participants will learn how to bring down
campaign cost by avoiding mass campaigning and targeting the customers with higher
propensity to buy;
Ability to create business value, enhance existing value and retain value generated by their
strongest assets-‘’valuable customer base’’.
Application Software
All our training programmes emphasize the impartation of methodological skills and are
absolutely hands-on utilizing relevant state-of-the-art software such as Microfit, R, Statistica, JMulti, Matrixer, SPSS, Rattle, SAS, excel, e-views, etc as the case may demand. Only Statistica
and Eviews 7 will be used for this particular programme. In addition to that, this course is
designed to utilize data and case studies on the Nigerian financial services sector particularly
the banks for training.
Pre-Requisites:
1. Basic knowledge of statistics;
2. Good practical knowledge of windows based computer applications. Participants are
expected to come with their own laptops if trainings take place in venues outside our
econometrics and analytics laboratory. Where this is however not possible we shall be
notified in time so that we can make arrangements for those who do not have their own
PCs.
The Modules
Day 1: Data Manipulation and Statistical Foundations
Data Manipulation
Review of Descriptive Statistics
Tests of Statistical Significance
Probability Sampling
Regression Analysis
Cluster Analysis
Day 2: Credit Risk Analytics
Credit Delinquency Scorecards
Customer Approval and Conversion
Optimal Loan Amount, Pricing and Loan Duration
Market Risk / Basel II Analytics
Portfolio Loss Forecasting
Collections Analytics
Day 3: Banking Fraud Analytics
Areas in Which Fraud Can Occur
What Fraudulent Activity Would Look Like in the Data
Data Sources Required to Test for Indicators of Fraud
"Fraud” Vs. “Erroneous” Claims, Information, Etc.
Fraud Detection as a Predictive Modeling Problem
Predicting Rare Events
Fraud Detection as Anomaly Detection, Intrusion Detection
Anomaly Detection
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Banking on Predictive Analytics
Rule Engines and Predictive Modeling
Text Mining and Fraud Detection
Day 4: Bank Marketing Analytics
Market Mix Modeling and Media Optimization
Campaign Effectiveness
Discrete Choice Modeling
Pricing Analytics
Channel Analytics
Churn Analytics
Product Bundling
Cross-sell/Up-sell Models
Purchase Driver Analytics
Introduction to Structural Equation Modeling
Day 5: Banking Operations
Procurement Analytics
Capacity and Resource Scheduling
Network and Logistics
Cost and Quality Management
Process Efficiency
Who Should Attend?
Credit Officers, Researcher officers, strategy and Planning officers, Retail Bankers, Portfolio
Managers, Asset Managers, Wealth Managers, Insurers and anyone in a liaison position for
business functions, credit approval, collections and other related roles in credit. The course will
also benefit those calibrating portfolio risk and working with retail or SME portfolios who monitor
overrides and gather feedback. It will be of particular relevance for persons in retail banking
functions working with sales, product teams, finance, compliance and credit operations.
Fees
The schedule of fees charged for this in house course is as presented below. Please note that the
fees cover course materials, facilitation fee, venue and lunch. Additional discounts beyond the
one offered below may be negotiated where the client chooses to provide both venue and
lunch for the course. Such additional discounts however will not exceed 10% of the amount
payable (net of the offered discounts).
Category
Range of Participants Size
Fee Per Participant(N)
Discount Offered (%)
A
100
+
120,000
30%
B
50
-99
120,000
20%
C
30
-49
120,000
15%
D
20
-29
120,000
10%
E
15
-19
120,000
0%
F
10
-14
150,000
0%
It is also important to note that regardless of the size of potential participants our maximum class
size for this course is 20 participants at a time. Therefore most participants will be trained in
batches.
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Banking on Predictive Analytics
Enquiries and Contacts
For further inquiries on the Banking on Predictive Analytics course kindly contact the following
persons:
Ebele
Vera
Olanma
Martin
08131218704 | [email protected]
08034546948, 01-8417203 | [email protected]
08033923041 | [email protected]
08033148722 | [email protected]
Office Location
Office address is as follows: ValueFronteira Limited, 4th Floor (rear wing); 42, Olowu Street,
Ikeja. [email protected]
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