1) The document discusses using machine learning and AI in banking and finance, specifically for a case study on a Tunisian bank (STB Bank). 2) It outlines several potential use cases for machine learning in areas like credit scoring, fraud detection, customer sentiment analysis, risk management, and operational efficiency. 3) The methodology discussed follows a CRISP process involving business understanding, data understanding, data preparation, modeling, evaluation, and deployment with feedback.

1. Use case : Machine Learning and AI
in banking and fnance
Dr Ahmed Rebai
Assistant Professor
Of Data Science
Esprit School of Engineering
29 December 2018
Dr Lotf Ncib
Assistant Professor
Of applied mathematics
Esprit School of Engineering

2. 1
2
3
4
5
6
Table of
Contents
Introduction
Retrospectives
STB Bank Use Case Presentation
Data science methodology
Project’s steps
Conclusion
2
Selecting DS methodology – Available data
Business understanding- Data's Phases– Modeling-Evaluation-Deployment-Feedback
Ahmed Rebai-Lotf cib

3. Introduction
3
Business
Banking and Finance solutions
Credit ranking system...
Data
Varity – Volume – Digitalization
Business Intelligence
Dashboarding – Intelligent visualization
Data science
Exploitation-Meaning-Prediction
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4. 4
public &
semipublic
sector
(52.5%)
foreign actors
(11.3%)
private
sector
(36.2%)
Financing of
Industrial
companies
18% of local
Market part
Investment
banking
Founded in
1958
In Tunis
bank's capital
participation
CEO Monsieur Samir Saied
Various instruments
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5. CRISP Methodology
5
Business Understanding
1st
Phase
Data Understanding
2nd
Phase
Data preparation
3rd
Phase
Deployment
Final phase
Evaluation
5th
phase
Modeling
4th
phase
CRISP
Methodolog
y
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6. 1
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3
4
The Master Plan
6
The new Data Science Methodology – (IBM vision 2018)
How can you use data to answer the question?
 Analytic Approach
What data do you need to answer?
 Data requirements
Where is the data coming from and how will you get it?
 Data Collection
Can you get constructive feedback into answering the question?
 Feedback
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7. Wanted Data
7
Structured
MySQL
Oracle
MSSQL
Semi-structured
CSV
JSON
XML
MongoDB
Unstructured
JPEG
PDF
MP3
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8. Tools that we will use
8
ETL + Reporting
Pentaho Data Integration
Dbeaver – PHPMyAdmin => MySQL database
Studio3T => MongoDB database
Power BI
Linux
Data Science
Python (numpy, pandas, matplotlib, sklearn,
tensorfow, keras, pytorch, textblob, senpy, nltk,...)
Google Cloud
Microsoft Azur
Amazon WebServices
Ahmed Rebai-Lotf cib

9. Business Understanding
9
Fraud Detection
Customers Sentiment
analysisAI & ML are used to identify sentiments in textual
data: in social media comments, news articles .
Risk Management
Operational
efficiency: i
ML and Graph theory can detect pattern
towards fraudulent operartions
(see Panama papers case HSBC Bank)
ML can predict risk arising out of banking exposures.
Risk could be either credit risk or fraud risk from
transactions or specifc customers.
A simple use-case is to convert hand-written forms into
machine readable data. This helps in reducing costs
signifi-cantly as most banking processes require lot of
paperwork.
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10. Analytic Approach - P1
10
 Semi-structured data contains :
 Clients’ information
 “Agences bancaires” ’ information
 DABs’ information
 Transactions’ information
 Find relation between clients and DAB in Transactions data.
 Week relationship between “Agences bancaires” and Transactions.
How can you use data to answer the question?
Develop a datawarehouse with this available data and try to centralize
the information in order to have a clear idea in Modeling phase
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11. Data Understanding
11
Reporting
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12. 1
2
3
4
5
Modeling – P2
12
Type of model : Supervised method
Algorithm: ARMA, ARIMA , SARIMA , SARIMAX,
Implementation : Python
Robustness & Evaluation = Stochasticity evaluation , Rsquared and
Accuracy AIC
Detection of Trend , Seasonality + residuals evolutions
Users 'number
forecasting
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13. 1
2
3
4
5
Modeling – P3
13
Type of model : Unsupervised method
Algorithm: CAH , KMEANS, Dbscan
Implementation : Python: sklean, Tensorfow
Robustness & Evaluation = silhouette score
Providing the clusters of users and then using them for group
charact-erization
Users’ profling
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14. 1
2
3
4
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Modeling – P4
14
Type of model : Supervised method
Algorithm: LDA , Logistic regression
Implementation : Python
Optimization & selecting model = GREEDY Wilks
Setting a score for each Reward / Loyalty based on the number of
transactions
Reward/Loyalty Scoring
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3
4
5
Modeling – P5
15
Type of model : Supervised method
Algorithm : NLP , Stemming , lemmatization
Implementation : Python
Robustness & Evaluation = MDT , IDF
Detect word weights that attract users
Knowledge text discovery
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16. 1
2
3
4
5
Modeling – P6
16
Type of model : Supervised method
Algorithm: collaborative fltering , Turicreate , CF
Implementation : Python
Robustness & Evaluations = RMSE , NDCG , Mean Reciprocal Rank
Recommend a fnancial product (specifc category) in a specifc period
, in a specifc region Recommend a user for a loyalty ofer.
Recommender system
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17. 1
2
3
4
5
Modeling – P7
17
Type of model : Supervised method
Algorithm : Decision Tree , Random Forest
Implementation : Python
Robustness & Evaluation = Roc Curve , Accuracy
Detect the conditions to take a ofer or not
 Need external tracking data of users in the web application:
Page views , clicks…
Boosting with Random
Forest
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18. 1
2
3
4
5
Modeling – P8
18
Type of model : Unsupervised method, Graph theory, discrete
mathematics.
Algorithm : Clustering, Community detection, Outliers detection
Implementation : Python
Robustness & Evaluation = Roc Curve , Accuracy
Detect suspicious operations
Fraud Detection
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19. Deployment
19
With Juputer-lab or External Web site
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20. Feedback
20
Can you get constructive feedback into answering the question?
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21. “In God we trust, all others must bring
data.”
W. Edwards
Derming
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22. Thank you for your
attention
29 December 2018
Ahmed Rebai-Lotf cib