Data Analytics in Financial Services and Banking

In today’s business world, data is gold! Businesses need quality data to decide, innovate, and compete. But just collecting data isn’t enough. The focus is on making sense of the data, finding key insights, and using them to boost company value. Data Analytics is a key solution to this, especially for the banking and financial service sectors. Financial institutions and the banking sector gain much by incorporating Data Analytics in financial services. They can make better decisions, manage risks, and offer personalized services. How is that?

Big data and analytics can help financial services companies save up to $500 billion in operating costs! These findings highlighted in the Worldmetrics.Org Report 2024 underline how Data Analytics can make a real difference to your bottom line.

This blog will show you just how Data Analytics in the finance sector makes all this possible.

What is the Use of Data Analytics in Financial Services and Banking?

81% of financial institutions believe that Data Analytics is a critical driver of business decisions. Why is that?

Businesses need data—market trends, customer insights, competitor analysis, to name a few. They must then turn this raw data into insights. These insights will be used to drive decisions, improve customer experience, and fuel growth. This finding finds immense relevance in the world of banking and finance. By examining relevant data, they can find useful insights that improve efficiency and keep them competitive.

The finance industry has been hit with significant challenges recently. These include unexpected global pandemics, industry convergence, and rapid technological changes. Thus, the profitability of financial businesses depends heavily on reducing risks and keeping a close eye on market trends. Data Analytics helps in this. It allows institutions to transform large amounts of unstructured customer and transaction data into valuable insights. These insights guide businesses in understanding crucial areas that affect their success.

Artificial Intelligence and Machine Learning are adding a new boost to what is possible with Data Analytics in financial services. This has led to automating data processing, pattern recognition, and prediction, which are of immense value to financial analysts. The demand for instant insights drives real-time analytics, especially in finance. Analyzing data at the source (e.g., IoT devices) reduces latency and optimizes real-time decision-making. There are limitless possibilities for data analytics in the financial sector.

Read more: Role of Data Analytics in Internet Of Things (IoT)

Top Benefits of Data Analytics in Banking and Finance

The financial sector is highly competitive, making it tough to survive. Customers increasingly seek out better, more convenient services. Data Analytics in financial services allows companies to make smart, data-driven decisions. It helps them discover new markets, create tailored products, become more efficient, and comply with regulations. Here are some benefits:

1. Effective Risk Management

In the modern banking landscape, a bank’s success is deeply intertwined with its risk management capabilities. Data Analytics is pivotal in this domain. Particularly in enhancing the bank’s ability to assess credit risks through advanced risk modeling. According to a McKinsey report, banks that effectively leverage risk modeling have seen a reduction in credit losses by up to 20% annually. These models are also integral in fraud detection and management. Furthermore, data-driven models provide a more profound understanding of operational and liquidity risks. This, in turn, safeguards the bank’s business operations.

2. Increase in Productivity

On the performance side, banks can utilize visual metrics of Data Analytics to assess key performance indicators (KPIs). This granular view enables banks to identify areas needing attention, leading to better resource allocation. This helps them achieve an increase in productivity.

3. Improved Decision Making

Data-driven sales and revenue performance analysis has been shown to improve decision-making. Regarding customer insights, Data Analytics empowers banks to understand customer behavior and trends, enabling personalized services. Tools like chatbots, virtual assistants, recommendation engines, and customer lifetime value predictions collectively enhance the customer experience. Banks prioritizing customer analytics have reported a significant boost in customer satisfaction scores.

4. Intelligent Business Strategy

Even simple decisions can significantly impact a bank’s revenue when guided by Data Analytics. For example, strategic placement of ATMs based on data insights can increase transaction volumes in underserved areas. Optimizing the number of tellers during peak hours has also improved operational efficiency.

5. Staying on Top of Compliance

Data Analytics enables faster responses to regulatory compliance queries, reducing the time required, which is critical for maintaining operational integrity and avoiding penalties.

Use Cases of Data Analytics in Financial Services and Banking

Data Analytics in banking and financial institutions has always been a useful tool. More so now, considering how much things have changed since the pandemic. The demand for data to be handled and processed more efficiently is ongoing. Customer data and touch points are changing fast. Our analysis models must adapt. Let’s look at how advanced analytics better serve customers’ ever-changing needs. Here are some use cases:

1. Compliance

Recent reports reveal that fines amounting to $28 billion have been imposed for sanctions violations and money laundering since 2008! The report also explains why.

• Risk classification is impeded by fragmented and unverified information
• Reviewing customer profiles is time-consuming and expensive
• 90% of alerts generated by Bank-KYC reviews are false positives.
• Less than 10% of Suspicious Activity Reports (SAR) are of use to authorities.

AI And Machine Learning techniques can be applied to automate customer due diligence and screening. Text mining techniques and Natural Language Processing can also streamline these efforts. This will help banks comply with Anti-Money Laundering Directives imposed by authorities and save costs on labor and fines.

2. Risk Assessment and Management

Effective risk management is crucial in banking operations. With data analytics, banks can analyze customer data, market trends, and economic indicators to assess better and manage risks. Banks that install data analytics in risk management can cut losses drastically. This highlights the efficiency and accuracy that these tools bring to financial decision-making. This includes evaluating credit risk, predicting loan defaults, and forecasting market volatility.

3. Hyper-Personalized Banking

72% of customers rate personalization as “highly important” in today’s financial landscape. Analytics enables micro-segmentation of information and allows banks and financial institutions to create hyper-personalized customer offerings.

4. Credit Modeling

New data access methods have emerged beyond traditional points like income and credit history. They include utility bills, social media, and spending habits on third-party vendor sites. This data gives a more accurate feel of a customer’s financial health. It also helps banks manage portfolios of corporate structures and SMEs. Data Analytics helps them detect early warnings, assess risk, calculate credit pricing, and more. AI and unstructured data play key roles here, offering a more detailed picture that can make credit risk assessments more accurate.

More than 60 percent of respondents said that they have increased their use of advanced data analytic techniques in credit portfolio management.

5. Fraud Detection and Prevention

Identity fraud caused $56 billion in losses to US financial services organizations in 2020. Surveys also reveal that the insurance fraud to claims ratio almost doubled during the pandemic, while it was 10% earlier. Data analytics tools can help predict and prevent fraud. They do this by reviewing past incidents and finding unusual patterns in transaction data, promoting a safer banking experience.

6. Predictive Analytics

Data Analytics can be a powerful tool in entering new markets, launching products, or adjusting services. Predictive analytics helps banks expect future trends and make strategic decisions. This can help banks stay competitive. It uses historical data and current market trends to do this.
For example, Bank of America uses predictive analytics to identify customers likely to default on loans. HSBC also uses predictive analysis to optimize fraud detection algorithms.

7. Churn Prediction and Customer Retention

As competition in banking and finance grows, keeping customers is as important as gaining new ones. Data analytics can help banks. Analyzing their behavior and transaction history can identify customers at risk of leaving. With this information, banks can act to retain these customers. They can offer personalized solutions to keep them engaged.

AI-powered Virtual Assistants:

Our world now keeps technology at the forefront of progress. Banks are quickly adapting to these changing trends in data services. Customers seek faster ways to serve themselves. Banks are now looking to AI to provide a long-lasting solution. They have found that solution in AI-powered virtual assistants. These virtual assistants can do it all. They can answer product questions, and process insurance claims in minutes. These tools are becoming essential for providing quick, accurate, and personalized customer service.

Data Analytics with Fingent

Fingent can help you make sense of your data. We specialize in creating products that can predict trends, analyze data, and present it clearly. This will help your business spot important patterns and plan for the future. Our experts excel in integrating AI and ML tools to give you a financial tool that will keep you abreast of the competition.

Whether using real-time data to improve performance or predicting changes in the market, our data solutions can help you. Your business can turn raw data into useful insights that lead to success. Work with Fingent and tap into a solution that will enable your company to make better decisions, improve how you work, and grow your business.

Contact us now!