This document discusses examples of artificial intelligence applications for fintech and retail startups. It summarizes using AI for coaching/advising clients, assessing client risk profiles, valuation models, pricing, credit approval/risk, customer churn/segmentation, contract analysis, and fraud detection. It then provides more detail on using decision trees and random forests for a credit approval classification model, including data acquisition, preprocessing, normalization, model training, evaluation using cross-validation and a confusion matrix, and achieving 87.8% accuracy. It recommends a machine learning book for further learning.

1. Inteligencia Artificial para
Startups Fintech and Retail
Hector Cuesta
@hmcuesta

2. ● Chief Data Scientist para X-Data
● ex Data Scientist para Grupo Salinas y Banco Falabella Colombia
● Autor del libro Practical Data Analysis
● Profesor invitado posgrado CIMAT
● Apasionado por la Robótica
4 datos acerca de mi...

6. Algunos Ejemplos de AI en Fintech
● Coach/Advisor
● Client risk profile
● Valuation models
● Pricing
● Credit approval / Credit Risk
● Churn / Customer Segmentation
● Contract Analyzer
● Fraud Detection

7. Coach / Advisor

8. Ejemplo de Aprobacion de Credito
Veamos cómo generar un modelo que te clasifique si un cliente puede recibir
un crédito o no, usando Decision trees y Random Forest.

10. Data Process
Data
Acquisition
Test set
Training set
Classification
Model
Model
Evaluation
parameter adjustment
Exploratory
Data
Analysis and
Data
Cleansing

11. Data preparation
Data preparation consist into a series
of steps that help us to get a usable
clean data, some of the steps like:
● Basic statistical details
● Histograms
● Attribute correlation
● Outliers
● Missing data

12. Preprocessing and Normalization
Numerical Vs Categorical Data:
Sci-kit Learn use only numerical data to it’s models, in this case we should use a label encoding to
turn the categorical attributes to numerical.
Data split:
Training set 2/3 Test set 1/3
Dataset: https://archive.ics.uci.edu/ml/datasets/credit+approval

13. Classification Models: Decision Tree based
“Divide-and-conquer” approach that predicts the value of a
target variable based on several input variables.
Applications:
● medical diagnosis
● classifying equipment malfunction
● risk of loan applicants
● price prediction

14. Classification Models: Random Forest
A random forest is a meta estimator that fits a
number of decision tree classifiers on various
sub-samples of the dataset and uses averaging to
improve the predictive accuracy and control over-
fitting.
Estimators : The number of trees in the forest.
Criterion : The function to measure the quality of
a split. Supported criteria are “gini” for the Gini
impurity and “entropy” for the information gain.

15. Evaluation Models
Cross-validation:
Confusion matrix:
Accuracy = (63 + 82)/165 = 87.8%
Sensitivity =82/(12 + 82) = 82.0%
Specificity = 63/(63 + 8) = 88.7%
Test setTraining setTraining set
Training set Test set
Test set
Training set
Training set Training set

16. Do you want to learn more ?
A great book for Machine Learning
models is: Hands-On Machine
Learning with Scikit-Learn and
TensorFlow
From O’Reilly
By Aurélien Géron

17. Gracias...
Hector Cuesta
@hmcuesta