Online Fraud Analytics Python Use Case

Fraud is a big problem for banks and financial institutions. Every year, billions of dollars are lost to fraudsters who find ways to exploit weaknesses in the system.

Digital fraud is the banking sector's primary challenge, leading to immense losses every year. As per McAfee's reports, cyber fraud currently damages the economy by USD 600 billion of GDP on a global basis.

But, How does this number feel so relevant? Because tons of data flows in daily, and guess whose job it is to track and report it to stakeholders like the Chief Financial Officer? 

It’s us Data Analysts!

Here, Data Analysis is not about just a Number; But a proven business solution which talks about the Charts, real time numbers and data driven insights.

In this python use case, we’ll talk about the Goals, Challenges and Objectives of the  Chief Financial Officer to provide meaningful insights they crave for.

Let’s get started!

About Dataset

Let’s Inspect our Fraud Analytics dataset. This dataset got all the deeds on transaction – transaction IDs, completion dates, types, subtypes, payment service providers (PSP), cities, amounts.

This dataset encompasses a variety of dimensions including temporal, financial, geographical, technical, and behavioral aspects of transactions, making it a potent resource for identifying and understanding fraudulent activities in financial systems. Here are some sample Rows & Columns of the Fraud Analytics Dataset.

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Here's a brief overview of the first few columns to set the stage for our analysis:

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Check Fraud Analytics Dataset to view all Columns 👉 Click Here

The Fraud Analytics Dataset contains over 50,000 Rows of transactions and 34 Distinct Columns. 30 of our Columns have String Data type, 2 of our Columns have Numeric Data Type and 2 of the columns have Date & Time Data Type.

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How to Improve Real-time Fraud Detection & Prevention System?

To start with the analysis of fraud detection & prevention system, it is necessary to follow the 4 factors of Data Analysis and that are : 

• 1. Identify the users or stakeholders for the dashboard.
• 2. Design Empathy Map to define Users' Goals and Challenges or pain points.
• 3. Identify Metrics or KPIs Matter the Most.
• 4. Understand the Objectives and Goals.
• 5. Ask Business Questions

In this article, we will explore how to improve such a system through a user-focused approach, taking the Chief Finance Officer (CFO) as a primary stakeholder.

Step 1: Identify the users or stakeholders for the analysis.

Define the User or stakeholder who will use the supply chain data story. In our case, We’ll use a Chief Finance Officer (CFO). Recognizing their diverse needs, challenges, and priorities becomes the cornerstone for tailoring an effective data story. 

User Persona : Chief Finance Officer

Responsibilities:

• Ensure the overall financial integrity of the organization & Safeguard financial assets against fraud activities.
• Make informed and strategic decisions based on accurate financial information.
• Evaluate risks and opportunities to support the organization's financial goals.
• Implement policies and procedures to mitigate legal and financial risks.
• Optimize resource allocation to maximize financial efficiency.
• Allocate budgets strategically to support business objectives.

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Needs:

• Access to real-time and accurate financial data for effective decision-making.
• Intuitive and user-friendly dashboards and tools for efficient monitoring and analysis.
• Advanced fraud detection systems that provide proactive alerts and mitigation strategies.
• Insights into emerging financial trends, risks, and opportunities to support strategic planning.

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Challenges: 

• Constantly evolving fraud tactics and increasing sophistication of cyber threats.
• Minimizing false positives in fraud detection to avoid unnecessary disruption to legitimate transactions.
• Managing financial security within budgetary constraints and ensuring cost-effectiveness.

Since, we have defined the User Persona & have mapped the needs, challenges and responsibilities. Our Next step will be to design an Empathy Map which will map the pain points of the user.

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Step 2: Design Empathy Map

Understanding the CFO's perspective is crucial for designing an effective fraud detection system. An empathy map helps us delve into their thoughts and feelings, allowing us to tailor the system to meet their specific needs.

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Step 3: Identify the Key Performance Indicators (KPI’s)

Numbers tell a story. To gauge the effectiveness of a system, focus on metrics and Key Performance Indicators (KPIs) that truly matter. For fraud detection and prevention, consider:

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Step 4: Understand the Goals & Objectives of User

Now, on the basis of Empathy Map and KPI’s, we need to define our goals and objectives of the Users. So, that it will align with the data story functionalities to ensure decision making. Here are the Key Objectives & Goals : 

Objective :

‍To enhance the security of financial transactions by employing advanced data analysis techniques. This includes real-time monitoring, anomaly detection, and proactive measures to identify, investigate, and mitigate potential instances of credit card fraud, thereby safeguarding the financial system's integrity.

Goals :

‍To optimize resource utilization for enhanced operational efficiency and profitability, support strategic initiatives by planning finance and investment decisions, minimize financial, fraud, and operational risks to safeguard assets and reputation.

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Step 5 : Ask Business Questions

Beyond KPI’s, organizations must engage in business-driven inquiry. This involves asking strategic questions that directly align with overarching business objectives. 

Ask the following questions:

1. How have fraud incidents fluctuated over the years?

Python Code : 

Visualization:

Metrics : Time Period, Volume Metric

Actionable Insight : 

• The graph shows a clear pattern of fraud incidents with constant spikes in July and August. This suggests the need for increased vigilance and preventive measures during these months. 
• The sharp decline after June may indicate effective countermeasures, which could be replicated to mitigate future spikes.

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2. Which types of Credit fraud are causing the most financial damage?

Python : 

Visualization:

Metrics : Credit Type, Total Amount of Fraudulent Transactions

Actionable Insight : 

• Credit card and Personal Loan fraud represent the highest total amounts, indicating these areas are particularly high-risk.
• Given the high amount of fraud in credit cards, there could be an opportunity to educate customers on safe credit card practices.

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3. Are there certain regions more prone to fraud incidents than others?

Python : 

Visualization : 

Metrics :Total Fraudulent Transaction Amount, Payer State

Actionable Insight : 

• States with higher fraudulent transaction amounts like Punjab, Bihar, and West Bengal may benefit from state-specific fraud prevention initiatives.
• Punjab, showing the highest total fraudulent transaction amount, should be subjected to more rigorous monitoring and investigative activities.

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4. What is the financial impact of fraud across different regions?

Python Code : 

Visualization : 

Metrics : Total Fraudulent Transaction Amount, Payer State

Actionable Insight : 

• Haryana, Tamil Nadu, and Odisha are the top states for fraudulent transactions, indicating the need for targeted anti-fraud initiatives in these regions.

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5. How does user behavior vary by time of day in terms of number of fraudulent transactions and fraudulent transaction amount?

Python Code : 

Visualization : 

Metrics : Time of Day, Percentage of Total Fraudulent Transaction Amount

Actionable Insight : 

• The largest share of fraudulent transactions occurs at night (31.0%), suggesting that fraudsters may prefer times when oversight may be lower and victims less vigilant.

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Conclusion

Enhancing our real-time fraud detection and prevention system transcends the realm of technical challenges—it's a collaborative effort.

We need to understand what the CFO worries about and ask the right questions. This way, we can create a system that not only meets but beats the CFO's expectations. That's how we keep our organization's finances safe in today's digital world.

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