The financial landscape is shifting rapidly, influenced by technology and data-driven insights. The competition in the financial services market is increasing and now it’s pretty evident from the proliferation of pure digital banks, Fintechs, and Big tech companies like Google, Amazon, Paytm, and more. Banks need to adapt, they need to adapt now in order to survive. They need to invest in technology, develop new products and services, and focus on customer service in order to stay ahead of the competition. Some of the key Use Cases of Data Analytics in Banking paint a picture of how to leverage the best of Data analytics for your department, operations, or the organization as a whole.

The worldwide banking market size was valued at $1.68 trillion in 2021 and is projected to reach $2.08 trillion by 2026, registering a CAGR of 3.9% from 2022 to 2026. Everyone wants a pie of this market and Banks need digitization as of yesterday.

Data analytics is playing a critical role in this transformation. Banks are using data analytics to transform their operations, improve customer experience, mitigate risks, and ensure compliance with regulations.

Data analytics Companies like DAAS LABS have set up a separate center of excellence for Banking Analytics and work on numerous Use Cases of Data Analytics in Banking. Banks also need to set up Data analytics COE and leverage Data’s potential to improve decision-making, tailor personalized experiences, and drive industry innovation is vast. Some more key facts to look at from a Data Perspective:

• The global banking industry generates 2.5 quintillion bytes of data every day.
• By 2025, the volume of data generated by the banking industry is expected to reach 463 zettabytes.
• 73% of banks are already using data analytics to improve their decision-making.
• By the end of 2023, 80% of banks will be using data analytics to personalize their customer experiences.
• The average bank customer generates 1.7 MB of data per day to 2.5 MB.

60% of banks believe that data analytics will be the most important driver of innovation in the banking industry over the next five years.

As per Allied Market Research report, the global data analytics in the banking industry was pegged at $4.93 billion in 2021 and is estimated to reach $28.11 billion by 2031, growing at a CAGR of 19.4% from 2022 to 2031. According to the McKinsey report on Corporate and commercial banking. It accounts for $2.3 trillion in revenues.

The Key Challenges in Banking Sector

The banking sector today grapples with multiple challenges, as customer expectations shift towards personalization and services that extend beyond the traditional scope of banking. Corporate and commercial clients now seek unique offerings that cater to their specific needs, encompassing not only loan and credit options but also transactional, fee-based services such as digital, real-time payments, spend analytics, and detailed liquidity and cash forecasts.

Furthermore, the clients expect their banks to demonstrate industry-specific expertise and assist them in navigating their global supply chains and addressing emerging issues such as decarbonization. Compounding these challenges are fintech companies disrupting the conventional banking model with their innovative services. Retail banking customers also face a number of challenges, including:

• The rise of digital banking: Retail customers are increasingly using digital banking channels, such as online and mobile banking. This puts pressure on banks to offer a seamless digital experience.
• The competition from fintech: Fintech companies are offering new and innovative financial services that are disrupting the traditional banking model. This puts pressure on banks to innovate and offer new products and services.
• The changing regulatory landscape: The regulatory landscape is constantly changing, which puts pressure on banks to comply with new regulations.
• The need for data security: Banks need to protect their customers’ data from cyberattacks. This is a major challenge, as cyberattacks are becoming increasingly sophisticated.

To stay competitive, banks need to address these challenges head-on. They need to offer a seamless digital experience, innovate and offer new products and services, comply with new regulations, and protect their customers’ data. By doing so, they can continue to provide their customers with the services they need and remain competitive in the years to come.

Data Analytics use cases in Banking

Data analytics, with its ability to decode patterns and provide insights, is poised to rescue and revolutionize the banking industry with various use cases and applications, guiding it toward an era marked by efficiency, personalization, and customer-centricity.

Personalized Banking

Data analytics enables banks to gather, analyze, and understand data from various customer touchpoints. This data encompasses customer behavior, preferences, spending habits, and interaction patterns, which banks can use to provide a more personalized and engaging customer experience. Based on a study by MuleSoft, 50% of consumers say that they would switch banks if they did not receive personalized experiences.

According to a study by Accenture, 75% of consumers are more likely to do business with a company that offers personalized experiences. Additionally, a study by McKinsey found that companies that personalize their customer experiences can increase customer retention by up to 85%.

Fraud Detection and Prevention

The average bank loses $1.3 million per year due to fraud. Data analytics can help banks to detect fraud with 99% accuracy. The banking sector is constantly grappling with fraudulent activities, which can severely dent customer trust and the financial bottom line. Data analytics plays a vital role in detecting anomalies and patterns that could indicate fraudulent transactions. Advanced analytics tools can learn from historical data to predict and identify potential fraudulent activity before it happens, thus enhancing security measures.

Risk Assessment and Management

Risk assessment and management is an integral part of banking operations. Data analytics can enhance this by analyzing customer data, market trends, and economic indicators to gauge potential risks. It can assess credit risk, evaluate the likelihood of loan defaults, and even predict market volatilities that could impact investment portfolios.

According to a study by the Association for Financial Professionals, 72% of banks use data analytics to assess and manage risk. Additionally, a study by McKinsey found that banks that use data analytics to manage risk can reduce their losses by up to 20%. Risk assessment and management is a complex and challenging task. However, data analytics can help banks to make the process more effective and efficient.

Customer Segmentation and Targeting

According to a study by the Aberdeen Group, banks that use data analytics for customer segmentation and targeting can increase their marketing ROI by up to 200%. Through data analytics, banks can segment their customers based on various parameters like income levels, spending habits, lifestyle choices, or risk profiles.

It can also aid in cross-selling and up-selling products and services to the right customer at the right time. Banks that use data analytics to cross-sell and up-sell products and services can increase their customer lifetime value by up to 30%. You can also Check How Data Analytics is changing marketing in the blog. https://scikiq.com/blog/data-analytics-use-cases-in-marketing-driving-growth-and-personalisation/

Compliance and Regulatory Adherence

Regulatory compliance is a complex and critical aspect of the banking industry. In 2021, the global banking industry paid $32.3 billion in fines for regulatory violations.

Compliance and Regulatory Adherence is one of the major use case in Banking analytics. Data analytics can help banks to simplify and improve their compliance processes. By automating data collection and processing, identifying discrepancies, and ensuring adherence to regulations like anti-money laundering (AML) and know-your-customer (KYC), data analytics can help banks to reduce the risk of non-compliance penalties. You can begin with SCIKIQ Data Assessments on Data Governance and Data Maturity.

Optimizing Operational Efficiency

Data analytics can play a significant role in improving operational efficiency within banking institutions. It can provide insights into bottlenecks in the system, inefficiencies in processes, and areas of waste. It can also enable banks to make data-driven decisions about resource allocation, process improvements, and cost optimizations. According to a study by the Association for Financial Professionals, banks that use data analytics to improve their compliance processes can reduce their compliance costs by an average of 20%.

Predictive Analytics for Future Strategy

Data analytics can forecast future trends based on historical data and current market dynamics. Predictive analytics can guide banks in strategy development, whether it’s about entering new markets, launching innovative products, or modifying existing services. It allows banks to stay ahead of the curve in a competitive industry.

Bank of America is using predictive analytics to identify customers who are likely to default on their loans. HSBC is using predictive analytics to optimize its fraud detection algorithms. By using predictive analytics, banks can stay ahead of the curve in a competitive industry and increase their chances of success.

Churn Prediction and Customer Retention:

One of the most significant uses of data analytics in banking is to identify customers who are most likely to leave and understand why. Predictive models are built using historical data, which includes various customer attributes like transaction history, complaint history, demographic data, etc.

If these models predict a high probability of churn for a specific customer, the bank can proactively address the customer’s issues and engage them with personalized offerings, thereby increasing the likelihood of retention. 75% of banks are already using predictive analytics in some capacity. (Source: IDC) By using predictive analytics, banks can stay ahead of the curve in a competitive industry and increase their chances of success. SCIKIQ is using Predictive Analytics to solve multiple use cases for customers.

Customer Lifetime Value (CLV) Prediction:

CLV is a prediction of the total net profit that a bank expects to make from any given customer. Data analytics allows banks to estimate the CLV based on a customer’s income, transaction patterns, product portfolio, and various other factors. 80% of banks use CLV to make strategic decisions. (Source: McKinsey & Company) and The average CLV for a bank customer is $2,500. (Source: IDC).

Knowing the CLV helps banks identify high-value customers and strategize their marketing efforts, risk assessments, and resource allocations accordingly. It enables them to cultivate long-term relationships with these customers by providing them with superior service and personalized products. As the banking industry continues to adopt data analytics, we can expect to see even more innovative ways to use CLV to improve profitability and customer satisfaction.

Algorithmic Trading and Investment:

Banks and other financial institutions rely heavily on data analytics for investment decision-making and algorithmic trading. Sophisticated algorithms analyze market trends, economic indicators, and financial news to predict stock price movements and execute trades at the optimal moment. 60% of all trading volume in the US stock market is now executed by algorithms. (Source: Tabb Group)

Algorithmic trading is the use of computer algorithms to automate the trading of financial instruments. This type of trading has become increasingly popular in recent years, as it allows banks and other financial institutions to make faster and more informed decisions. The average algorithmic trading algorithm makes 100 trades per second. (Source: QuantInsti)

How does Data management work in the Modern Digital world?

The use of data analytics in trading can maximize profits, minimize risks, and enable high-frequency trades that would be impossible for human traders to execute. Furthermore, it can assist in portfolio management by identifying the best combination of investments to achieve the desired return at a specific risk level.