Credit risk assessment system ppt

1. CREDIT RISK ASSESMENT
SYSTEM USING MACHINE
LEARNING
PROJECT TITLE :

3. PROJECT 0VERVIEW :
Credit risk is the risk of a borrower defaulting on a loan, affecting
financial institutions' stability and profitability. Credit risk
assessment uses income and credit history variables. Historically,
manual auditing and statistical techniques were costly and error-
prone. Machine learning (ML) can enhance credit risk assessment
by learning from data, identifying hidden patterns, and producing
accurate results.

4. PROBLEM STATEMENT
Despite the growing demand for credit services in
Tanzania, traditional credit assessment methods exhibit
limitations in accurately evaluating borrower risk profiles,
leading to suboptimal lending decisions and increased
financial risks for lending institutions. Manual
assessment processes often lack comprehensive insights
into individual creditworthiness, resulting in higher
instances of defaults and inefficiencies in credit
provisioning. Moreover, the absence of tailored credit
scoring mechanisms that account for regional nuances
and evolving economic conditions poses a significant
problem in promoting inclusive and responsible lending
practices

5. OBJECTVIES
 MAIN OBJECTIVE :
The main objective of this project is to
develop a machine learning-based credit risk
evaluation system that can accurately and
efficiently predict the default probabilities of
borrowers and provide actionable insights
for lenders.

6. SPECIFIC OBJECTIVE
To create a dataset of transactions, historical information, and
other needed data.
To process the dataset for implementation
To create a model which will identify patterns and relationships
between collected financial data
To integrate the system and the model .

7. LITERATURE REVIEW
 THEORETICAL LITERATURE/FRAMEWORK
 The development and implementation of Credit risk assessment system address
various challenges and opportunities within the financial sector. The theoretical
literature and frameworks surrounding this problem highlight several key areas that
the system seeks to improve, including manual assesment, data processing, and
overall credit risk assesment efficiency. This should be used to improve credit risk
assessment by analysis of large and complex datasets, discovering hidden patterns,
and providing accurate and reliable outcomes.

8. KEY TERMS
 - Credit risk: This refers to the potential of financial loss resulting from a borrower's
failure to repay a loan or meet their financial obligations.
 - Credit scoring: This is a method used by lenders to evaluate the creditworthiness
of a borrower based on various factors, such as credit history, income, and other
relevant data, to determine the likelihood of repayment.
 - - Deep learning: Refers to a subset of machine learning that uses neural
networks with multiple layers to understand and represent complex patterns in data,
often used in tasks like image and speech recognition.

9. METHODOLOGY
RESEARCH APPROACH
Mixed research approach
 Because ,Utilizing a mixed research approach in our CREDIT RISK ASSESMENT USING
MACHINE LEARNING can offer valuable insights into user needs, preferences, and the overall
effectiveness of system application.

10. RESEARCH METHOD
Agile Model.
Pros: Iterative, adaptable, encourages user
feedback and continuous improvement
Cons: Can be less structured, requires strong
project management and communication, may
not be suitable for all research phases

11. SYSTEM IMPLEMENTATION
Prototyping :to Develop a functional prototype to visualize and test
key features
Development: Implement the system according to design
specifications
Testing: Ensure the reliability and correctness of the implemented
system

12. SYSTEM IMPLEMENTATION
CODING
Front-End Development :include
languages like HTML, CSS,
JavaScript,Frameworks/Libraries
Back-End Development :
Languages: Node.js (JavaScript),
Python, Java, Ruby, PHP ,
Frameworks
Database Management: database
includes MySQL.
TESTING
Train-Test Split
 Because:Testing the model in a real or
simulated deployment environment
to ensure its compatibility,
scalability, and reliability within the
proposed system architecture.

13. LOGICAL DESIGN ARCHITECTURE
User Interface design
Presentation design
Application Logic design
Database design

14. SYSTEM ARCHITECTURE

15. SYSTEM REQUIREMENT
HARDWARE REQUIREMENTS
 Processor: Multi-core processors core
i5
 RAM: 8 GB
 Storage: SSD for fast data access
 Network: High-speed internet
connection
 SOFTWARE TOOLS REQUIREMENTS
 Operating Systems: Windows.
 Backend Technologies(Python,
mySQLdatabase and apache server)
 Frontend Technologies: includes
JavaScript Frameworks(React,
Angular, or Vue.js for frontend
development)

16. PROJECT TIMELINE & BUGET
Gathering requirements from 3-5 week
Quick design form from 6-10 week
Building prototype from 10-12 week
Initial user evaluation from 12-13 week
Refining prototype from 13-15 week
Implementing system and maintenance 15 onward
Budget :