1. AI-Driven Fraud Detection System
Overview:
AI-Driven Fraud detection is one of the most critical areas in banking. With financial institutions dealing with large volumes of transactions every day, identifying fraudulent activity in real-time is vital. Traditional fraud detection systems often rely on static rule-based methods, which become outdated as fraudsters evolve. This Gen AI project leverages machine learning and deep learning techniques to improve fraud detection by continuously learning from data, identifying suspicious activities, and preventing fraud even before it happens.
Project Approach:
The project begins by collecting historical transaction data from the bank’s systems, which includes credit card transactions, wire transfers, mobile banking activities, and more. Data is cleansed and pre-processed to remove noise and irrelevant data points. A deep learning model, such as a Long Short-Term Memory (LSTM) network, is trained on this dataset to understand temporal patterns in transactions. Convolutional Neural Networks (CNNs) are employed for feature extraction from high-dimensional data, such as image-based checks or fingerprint-based verification’s.
The model also integrates Natural Language Processing (NLP) to analyse customer service interactions, emails, and chat logs, identifying potential phishing attempts or social engineering tactics. This creates a holistic approach to fraud detection, which goes beyond just transaction analysis.
Real-time Deployment:
Once trained, the model is deployed on AWS cloud infrastructure to ensure scalability and real-time processing. As transactions flow through the system, the AI model continuously monitors and flags suspicious activities. The system sends instant alerts to banking security teams, enabling immediate intervention.
Key Features of the AI System:
- Adaptive Learning: The AI learns from new types of fraud, continuously improving its detection capability.
- Low False Positives: By analysing large datasets and learning patterns, the system significantly reduces the occurrence of false alerts.
- Customer Profile Creation: The AI builds dynamic customer profiles based on behaviour patterns, allowing it to detect anomalies more effectively.
- Predictive Capabilities: The system not only detects fraud but also predicts potential fraud based on emerging transaction patterns.
Impact on Banking Operations:
Banks benefit from an immediate reduction in fraudulent activities, with increased precision in flagging high-risk transactions. False positives, which often frustrate legitimate customers, are reduced, providing a seamless banking experience. Additionally, the system’s predictive capabilities allow banks to proactively address potential fraud before it occurs, improving both customer trust and regulatory compliance.
Outcome:
One of the top national banks that implemented this system saw a 60% increase in fraud detection efficiency and a 50% reduction in false positives within the first 12 months. As a result, the bank saved millions in potential fraud losses while improving customer satisfaction and reducing overhead for the fraud investigation team.
2. Power BI Interactive Loan Portfolio Dashboard
Overview:
Managing large loan portfolios requires a clear and real-time view of various key performance indicators (KPIs) like delinquency rates, loan-to-value ratios, and non-performing loans. Traditionally, banks rely on static reports that take days or weeks to generate, making it hard to respond quickly to changes in the loan portfolio. The Power BI Loan Portfolio Dashboard is designed to provide banking executives with a dynamic, data-driven view of their entire loan portfolio.
Project Approach:
This project involves integrating multiple data sources, such as Loan Origination Systems (LOS), Customer Relationship Management (CRM) systems, and the bank’s financial database (Azure Synapse Analytics). Power BI pulls in the raw data and transforms it into easily digestible insights through interactive visualizations.
The Power BI dashboard is designed with multiple drill-down capabilities. Users can view high-level portfolio KPIs or drill down into specific segments, such as:
- Loan types (mortgages, personal loans, auto loans, etc.)
- Delinquency categories (30, 60, 90 days past due)
- Risk segments (low, medium, high risk)
- Geographical regions (state, city, branch-level data)
- Individual customer performance
Key Dashboard Elements:
- Non-performing Loans (NPL) Ratios: Tracks the percentage of loans that are in default or close to default.
- Loan-to-Value (LTV) Ratios: Provides insights into how much risk is associated with the loan in comparison to the asset’s value.
- Delinquency Rates: Shows overdue loans categorized by age (30, 60, 90 days past due) to identify trends in repayment defaults.
- Profitability Analysis: Breaks down the profitability of different loan products, helping management make strategic decisions.
Advanced Analytics and DAX Formulas:
The dashboard utilizes DAX (Data Analysis Expressions) to calculate key metrics, such as NPL ratios and rolling averages for loan delinquency. The visualizations include trend lines, heatmaps for regional performance, and gauges for KPI status indicators.
Impact on Decision Making:
Bank executives can make faster and more informed decisions based on real-time data. For instance, if delinquency rates spike in a particular region, the bank can respond by tightening lending policies or increasing customer outreach. Additionally, risk managers can better manage the loan portfolio’s health, minimizing exposure to non-performing loans and improving the overall profitability of the bank’s loan products.
Outcome:
A leading financial institution implemented this Power BI solution and reported a 15% improvement in loan recovery efforts within the first six months. Moreover, their risk management team was able to identify and mitigate risks 30% faster than before, allowing them to maintain a healthier loan portfolio and improve their overall credit risk management.
3. Power BI – Customer Credit & Collection Dashboard
Overview:
In the banking sector, efficient management of customer credit and collections is critical for maintaining liquidity and minimizing risk exposure. This Power BI Customer Credit & Collection Dashboard project is designed to empower credit card collections teams by providing a consolidated view of credit and collection metrics across multiple collection agencies. By leveraging Power BI, this dashboard enables credit teams to track outstanding balances, overdue amounts, and credit exposure in real-time, ensuring better decision-making and faster recovery of overdue amounts.
Key Features of the Dashboard:
- Aggregated View of Customer Credit and Collections
The dashboard consolidates data from various sources, giving the collections team a single view of credit limits, customer balances, overdue payments, and overall credit exposure. This aggregated view is essential for tracking overall portfolio health and ensuring that no overdue accounts are overlooked. Credit officers can easily see metrics related to customer credit limits, outstanding amounts, and payment histories.
- Key Credit & Collection Metrics:
The dashboard highlights crucial KPIs for monitoring collection activities:
- Day Sales Outstanding (DSO): Indicates the average number of days it takes to collect payments after a sale has been made.
- Balance Overdue: Displays the total outstanding balances that are past due.
- Credit Exposure: Visualizes the risk exposure based on customer credit limits and their current usage.
- Overdue Customers: Provides insights into the number of customers who have missed payment deadlines, allowing collection teams to prioritize high-risk accounts.
- Detailed Customer Insights:
The dashboard allows drill-down into specific customer profiles, showing individual credit limits, balances, overdue amounts, and historical payment behaviours. This is invaluable for the credit team when contacting customers to collect overdue payments, as they have all the relevant data at their fingertips.
- Performance Tracking by Region & Agency:
The system provides geospatial insights into credit and collections performance by region or by the collection agency. This helps the bank identify trends or specific regions where collections are lagging, enabling targeted interventions. For instance, the map visualizations highlight regions with the highest overdue balances, allowing credit teams to focus their efforts accordingly.
Visualizations and Data Insights:
- Balance Overdue by Region:
This section of the dashboard presents a geospatial view of overdue balances across different regions, providing insights into the geographic distribution of outstanding debt. It helps banks identify high-risk areas and adjust collection strategies accordingly.
- Credit Limit Usage by Region:
Visualizing the usage of credit limits across regions, the dashboard enables the tracking of credit consumption trends, ensuring the bank can monitor where high credit utilization might lead to potential defaults.
- Delinquency Aging Reports:
The aging report categorizes overdue amounts into buckets based on how many days they have been past due (e.g., 30, 60, 90+ days overdue). This helps credit teams prioritize collections efforts, focusing on accounts that are approaching critical delinquency thresholds.
- Collections Overview:
A pie chart provides a breakdown of the collection statuses, showing amounts collected, outstanding, and disputed. This metric helps credit teams track the effectiveness of collection strategies over time.
- Payment and Collection Trends:
A trend graph at the bottom of the dashboard showcases the expected payments and the actual collections over time, giving insight into the collections’ progress compared to projections. This feature enables data-driven decision-making and helps collection teams identify under-performing periods.
End Users:
The dashboard is primarily designed for the Credit Card Collections Team, but it can also be utilized by:
- Credit Risk Managers: To assess credit risk exposure and analyse customer behaviour patterns.
- Finance Teams: To understand liquidity and cash flow management based on overdue collections.
- Management Executives: To gain a high-level overview of the bank’s credit portfolio health.
Business Impact:
By implementing this Customer Credit & Collection Dashboard, the bank can achieve:
- Improved Collection Efficiency:
With real-time data at their disposal, collection teams can act faster, reducing the amount of time it takes to recover overdue payments. This improves liquidity and cash flow for the bank.
- Reduced Credit Risk:
By keeping track of credit exposure and overdue balances, risk managers can identify high-risk accounts earlier, allowing them to take preventive actions before defaults occur.
- Data-Driven Decisions:
The dashboard helps teams move away from manual processes and static reports, empowering them with real-time, actionable insights. This leads to more informed decision-making and better customer management.
- Geographic and Customer Segmentation:
The ability to segment data by region and customer profile allows the bank to tailor its collection strategies based on regional trends and specific customer behaviours. For example, focusing more collection efforts in regions with higher overdue amounts.
- Enhanced Customer Relationship Management:
With a detailed view of each customer’s credit usage and payment history, the collections team can personalize their approach to resolving overdue payments. This not only improves recovery rates but also maintains a positive relationship with the customer.
Outcome Example:
After deploying the Power BI Customer Credit & Collection Dashboard at a major retail bank, the collections team reported a 20% increase in overdue payments recovered within the first three months. Additionally, the bank saw a 15% improvement in DSO (Day Sales Outstanding), resulting in improved cash flow and reduced risk exposure. The data-driven insights enabled the collections team to focus on high-priority accounts, reducing the overall delinquency rate by 10%.
Conclusion:
The Customer Credit & Collection Dashboard is a powerful tool for banking institutions aiming to streamline their credit and collection processes. With a comprehensive, real-time view of key metrics, credit teams can improve their performance, reduce risk, and ensure faster recovery of overdue amounts. By integrating this Power BI solution, banks can optimize credit management operations, providing both immediate and long-term benefits to their financial health.