This solution should aim to expand access to financial services by providing a platform that evaluates the creditworthiness of individuals who may not have traditional credit histories. By leveraging alternative data sources such as mobile phone usage patterns, social interactions, and utility bill payments, the system can assess an individual's credit risk more inclusively. The expected outcome is a prototype or proof-of-concept that promotes financial inclusion and responsible lending, maintaining low default rates while fostering economic growth for underserved communities.

The goal is to create a platform that empowers customers to understand and manage their finances better. It should integrate spending analysis tools to track transactions, goal-setting functionalities to set and monitor financial targets, and budgeting tools that adapt based on user behavior. The platform should offer tailored insights and recommendations that help users make informed financial decisions. The expected outcome is a prototype that enhances customer engagement for banks by helping users achieve financial stability and growth.

This solution should utilize machine learning algorithms to analyze multiple factors such as transaction location, amount, frequency, and user behavior to detect potential fraudulent activity. The system should be designed to provide real-time alerts and prevent suspicious transactions while ensuring that legitimate transactions are not disrupted. The objective is to create a functional model that enhances security measures for banks, reduces false positives, and protects user trust without compromising transaction speed.