Projects / AI Innovation Challenge

Beyond Numbers: Understanding Financial Vulnerability via Transactional Patterns using Machine Learning

Challenge Start: Nov 2nd


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Developing a data-driven solution to identify financial transaction patterns and understand vulnerabilities within individuals and households. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

The problem

Financial vulnerability, a pervasive issue affecting individuals, households, and communities, stems from a combination of income instability, limited savings, high debt levels, low financial literacy, and restricted access to financial services. This phenomenon leads to numerous adverse effects on both the affected individuals and the larger society.

Problem statement:

The lack of effective strategies to address financial vulnerability hampers individuals’ economic stability, mental well-being, and overall societal resilience. Without adequate measures in place, individuals and communities are ill-equipped to cope with financial shocks, resulting in a range of negative consequences that permeate various aspects of their lives.

Scope and impact:

Financial vulnerability not only exacerbates existing social inequalities but also contributes to reduced economic mobility, psychological stress, and hindered health outcomes. This problem has far-reaching implications, from economic instability to the perpetuation of generational hardships.

Objective:

The primary goal of this project is to develop and implement a comprehensive strategy that addresses financial vulnerability at both the individual and community levels. This strategy aims to empower individuals with the knowledge, resources, and tools necessary to mitigate the impact of financial shocks, make informed financial decisions, and build a foundation for greater economic stability.

The project goals

This project aims to develop a data-driven approach to identifying and understanding financial vulnerabilities in individuals and households. By creating a proof of concept, segmenting vulnerable customers, and providing insights into different income aspects, the project will contribute to targeted interventions and strategies that promote financial stability and resilience. The identified deliverables will serve as valuable tools to address financial vulnerability systematically and effectively.

The main goals of this Omdena-Serene Challenge are:

  • Exploration of reliable data sources and data collection.
  • Identify Vulnerabilities:
    • Systematically identify various factors contributing to financial vulnerability, including income instability, low savings, high debt levels, and limited financial literacy.
    • Understand the interconnectedness of these factors and their impact on individuals’ overall financial well-being.
  • Accurately Identify Vulnerable Customer Base:
    • Develop methodologies to accurately identify individuals or households vulnerable to financial instability.
    • Utilize data-driven approaches to create targeted profiles of customers who exhibit signs of financial vulnerability.
  • Analyze Financial Patterns in Data:
    • Analyze transactional data to extract meaningful insights into customers’ financial behaviors.
    • Identify recurring patterns that might indicate financial stress, such as irregular income sources or high credit utilization.
  • Segment Vulnerable Customers:
    • Categorize customers based on their vulnerability levels, considering multiple parameters like income sources, debt-to-income ratios, and savings habits.
    • Create distinct segments to tailor interventions and assistance strategies for specific groups.
  • Explore Different Income Aspects:
    • Investigate various income aspects, such as stability, sources, and consistency, to uncover different narratives within the data.
    • Understand how diverse income scenarios contribute to financial vulnerability.
  • Understand Data Challenges:
    • Recognize and address challenges related to data quality, completeness, and accuracy in financial transaction records.
    • Develop strategies to handle missing or inconsistent data points effectively.

Deliverables:

  • Proof of Concept (PoC):
    • Develop a functional proof of concept demonstrating the feasibility of identifying financial vulnerabilities through transaction patterns.
    • Showcase the methodology’s effectiveness in capturing key vulnerability indicators.
  • Identification of Implicit and Explicit Vulnerabilities:
    • Create a framework to identify both explicit vulnerabilities (e.g., high debt) and implicit vulnerabilities (e.g., irregular income patterns).
    • Showcase the ability of the framework to differentiate between these types of vulnerabilities accurately.

Why join? The uniqueness of Omdena AI Innovation Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works

First Omdena Project?

Join the Omdena community to make a real-world impact and develop your career

Build a global network and get mentoring support

Earn money through paid gigs and access many more opportunities



Your Benefits

Address a significant real-world problem with your skills

Get hired at top companies by building your Omdena project portfolio (via certificates, references, etc.)

Access paid projects, speaking gigs, and writing opportunities



Requirements

Good English

A very good grasp in computer science and/or mathematics

Student, (aspiring) data scientist, (senior) ML engineer, data engineer, or domain expert (no need for AI expertise)

Programming experience with Python

Understanding of Machine Learning and/or Data Analysis



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