3 Financial Data Analytics Project Ideas for your Resume

But before we begin, if you're someone who wants to build projects for Data Science Portfolio then keep your tabs on what's happening in the world of Data Science & Businesses, and join our Free Mentorship Session to get in touch with industry experts and understand what's hot in the market! 😉

‍

‍

It's becoming increasingly evident that managing financial data is becoming more complex. While it poses challenges for Banks and Insurance Businesses, it also holds significant potential.

According to a recent study, the global financial analytics market has grown from $7.6 billion in 2020 and is expected to reach $19.8 billion by 2030. It is growing at a CAGR of 10.3%, which is a quick pace. Now is the right time to understand what financial analytics is and enter the industry.

It sees a constant influx of information daily, and it falls upon data analysts like us to track and present this data to stakeholders, including supply chain managers. Reporting goes beyond just presenting numbers and charts; it involves understanding the specific needs and preferences of the Financial managers.

Fortunately, in this scenario, we can assist in identifying their requirements, desires, goals, and objectives, thereby providing them with the insights they seek.

Let's begin by delving into understanding the data better.

‍

Project 1: Vehicle Car Insurance

This project focuses on a detailed analysis through a five-step strategy that includes identifying stakeholders, creating empathy maps, determining key performance indicators (KPIs), setting objectives and goals, and formulating strategic questions. This approach aims to better understand customer needs, improve service delivery, and drive business growth by making data-driven decisions in the car insurance domain.

1. Define User Persona for this Project

Let's say, for this use case our Business Persona will be - Insurance Manager. We will look into the data with an Insurance Manager perspective so that we have a starting point on how we perceive the data.

‍

2. Design an Empathy Map

Once, let's get into the mind of our Insurance Manager with an Empathy Map:

‍

3. Define Objective for Business Persona

Now, let’s define the objective of our Insurance Manager based on the empathy map we just created.

Objective : The Insurance Manager aims to improve how well the insurance portfolio does by focusing on the areas that make the most money and making better models for figuring out risks. They plan to use what customers say to make their insurance products and customer service better, hoping to make customers happier.

‍

4. Identify your KPIs

Next, let's measure success with some KPIs:

‍

Finally, let's ask some burning questions during our Exploratory Data Analysis (EDA):

Q1. What is our overall performance in vehicle Insurance?

Observation: We have 16891 customers with $34.6 billion insured amount and 35066 policies running.

‍

Q2.How are my customers Distributed over Banks?

Observation: Amex cc is popular amongst customers and they like the Direct Debit Facility of payment mode.

‍

Q3. What is our customer's choice of policy type for their vehicle?

Observation: Our customers prefer TP Renewal more and their favourite vehicle type is SUV.

‍

Q4. Which regions and categories contribute the most to sales?

Observation: Luxury and RTI Package are popular in high end customers whose top 3 vehicle type choices are Sports Car, MPV, SUV.

‍

Explore full Vehicle Insurance Analysis Data Story Here👇

‍

Project 2: Customer Loan Analysis

The project analyzes Bank's workforce using customer loan data to enhance loan operation efficiencies. It involves data-driven insights into operational focus and market strategies, centered around stakeholder identification, empathy mapping, KPI determination, and goal setting to optimize resource utilization and foster business growth.

1. Define User Persona for this Project

Let's say, for this use case our Business User will be - Loan Manager

‍

2. Design an Empathy Map

Once, let's get into the mind of our Loan Manager with an Empathy Map:

‍

3. Define Objective for the use case

And now, let’s define the objective based on the Loan Manager Empathy Map

Objective : Enhance workforce efficiency and performance in the loan management department to optimize resource utilization and drive business growth. This objective aims to improve loan manager productivity, streamline loan processing, and recognize top performers. By implementing targeted training programs and performance evaluations, we seek to achieve measurable improvements in loan disbursal rates and customer satisfaction within the next quarter.

‍

4. Identify your KPI's

Next, let's measure success with some KPIs:

‍

Finally, let's ask some burning questions during our Exploratory Data Analysis (EDA):

1. How is our workforce distributed across all regions? Can we compare it to our total loan amount disbursed?

Observation: With 40 Sales managers, 490 Sales Representatives and 691 Dealers across the country have reached 16998 customers.

‍

2. What is the average loan amount and number of customers converted by sales managers? Are there any sales manager performance we need to investigate?

Observation: There seems to be a difference between our top sales manager i.e. Rakesh Kumar and other remaining managers.

‍

3. Who are our top 10 sales managers in terms of loan amount and sales rep and dealers under them?

Observation: Rakesh Kumar has the highest Loan Amount in comparison to others with $765.7m across 10 sales manager.

‍

4. What is the reason behind the exceptional performance of our top sales manager?

Observation: Since it seems Rakesh Kumar is operating in all the regions across the country, he has exceptional performance.

‍

‍

Explore full Customer Loan Analysis Data Story Here👇

‍

Project 3: Online Fraud Analysis

The project analyzes Bank's workforce using customer loan data to enhance loan operation efficiencies. It involves data-driven insights into operational focus and market strategies, centered around stakeholder identification, empathy mapping, KPI determination, and goal setting to optimize resource utilization and foster business growth.

1. Define User Persona for this Project

Let's say, for this use case our Business User will be - Chief Finance Officer

‍

2. Design an Empathy Map

Once, let's get into the mind of our Chief Finance Officer with an Empathy Map:

‍

3. Define Objective for the use case

And now, let’s define the objective based on the Chief Finance Officer Empathy Map

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.

‍

4. Identify your KPI's

Next, let's measure success with some KPIs:

‍

Finally, let's ask some burning questions during our Exploratory Data Analysis (EDA):

1. How have fraud incidents fluctuated over the years?

Observation :

• 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.

‍

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

Observation :

• 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.

‍

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

Observation :

• 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.

‍

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

Observation :

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

‍

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

Observation :

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.

‍

Explore full Online Fraud Analysis Data Story Here👇

‍

We at Super AI are on a mission to tell #1billion #datastories with their unique perspective. We are the community that is creating Citizen Data Scientists and we are committed to help you become one of the Top 1% data storytellers.

With Saurabh Moody and Preksha Kaparwan you can start your journey as a Citizen Data Scientist.

All the Best❤️

‍