Predictive Analytics in Banking Market Size, Share & Trends Analysis Report By Component (Solution, Service, By Deployment Model, On-Premise, Cloud), By Organization Size (Large Enterprises, Small and Medium Enterprises), By Applications (Fraud Detection and Prevention, Customer Management, Sales and Marketing, Workforce Management, Others) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2024-2032

Market Overview

The global predictive analytics in banking market size was valued at USD 3,013.43 million in 2023. It is expected to reach USD 16,342.54 billion in 2032, growing at a CAGR of 20.6% over the forecast period (2024-32). The adoption of predictive analytics in banking is driven by the need to enhance customer insights, manage risks more effectively, improve operational efficiency, comply with regulations, stay competitive, leverage technological advancements, improve financial performance, and enhance customer experience. These drivers collectively contribute to the growing importance and implementation of predictive analytics in the banking sector.

Predictive analytics is an advanced analytics technology that identifies the current trend of organizations and controls the organization's financial risks using historical and current data. Predictive analytics uses various techniques, such as statistics, data mining, data modeling, machine learning, and artificial intelligence. These techniques are widely adopted for identifying financial uncertainty, accidents, strategic management errors, and legal liabilities. Banks and financial institutions widely use predictive analytics techniques to track customer behavior and recognize emerging issues by analyzing unstructured data collected from customer emails, survey responses, banker notes, and call center transcripts. It aids banks and other financial institutions develop a customer experience strategy to improve their communications and banking services. These improvements in customer experience strategy help boost profit and help the banks increase customer retention.

Highlights

• The solution dominates the component segment. 
• On-Premise dominates the deployment mode segment.
• Large enterprises dominate the organization size segment.
• Customer management dominates the application segment.
• North America is the highest shareholder in the global market.

Market Dynamics

Global predictive analytics in banking market drivers

Increasing Adoption of Advanced Technologies for Fraud Detection

A significant rise in fraudulent activities has been observed in banking and financial institutions in recent years. Customers have started using banking services across various channels, leading to increased bank frauds such as money laundering, payment card fraud, and fraudulent loans. However, advanced technologies such as predictive analytics and machine learning algorithm-based fraud detection solutions can reduce such fraudulent activities. Machine learning-based fraud detection helps banks detect online fraud and quickly suggests the necessary actions to decision-makers. Several large-sized banks have started using predictive analytics-based fraud detection software to detect fraudulent activities across various channels involved in payment processing.

In addition, these institutions are also using predictive analytics software in mobile apps for remote ordering or banking and paying for goods and services. For instance, Danske Bank has implemented Teradata's fraud detection solution, which integrates machine learning and AI algorithms. The solution helped Danske Bank in detecting real-time fraud detection by 50%. As a result, a greater number of such implementations of predictive analytics for fraud detection among banking and financial institution has been driving the growth of predictive analytics in the banking market growth.

Surge in the Number of Risk Management Functions

Risk management has been among the most challenging functions of banking institutions for several decades. Any pitfalls in managing risks by these organizations can adversely affect the profit of an organization. These global financial institutions have increased their focus on handling several risks, such as customer, operational, credit, and operational risks. The banking industry generates a huge volume of data daily that can be used by predictive analytics to develop several risk functions, such as internal audit, stress testing, bank failure prediction, and operational and liquidity risks.

In addition, predictive analytics used in the banks supports the initial discovery of high-risk accounts to reduce fraudulent and default cases. For instance, one of the large banks in the U.S. has reduced its loan default calculation time for mortgage lending. The organization observed a decrement in the default calculation time of 10 million loans from 96 to 4 hours using predictive analytics technology. Therefore, the growing demand for risk management has enabled to adopt the predictive analytics solutions, thus driving the market growth.

Global predictive analytics in banking market restraint

Implementation Issues of Predictive Analytics Software

Several complexities in the installation and configuration of predictive analytics software have been observed during the deployment of these solutions among banking institutions in recent years. These institutions face several difficulties in implementing predictive analytics software due to less technical expertise. Most of these technologies are complex to deploy among banks; thus, they require data scientists or analyst experts to understand data analytics tools better. However, the lack of required skills and expertise in these technologies hinders the growth of predictive analytics in the banking market.

Global predictive analytics in banking market opportunities

Integration of Artificial Intelligence in Mobile Apps

Integrating advanced technologies like AI in mobile banking apps has helped customers analyze account information and personalized financial guidance. In addition, these AI-based mobile banking apps have increased the capabilities of banking institutions to advance customers' financial wealth, have a more comprehensive view of finances, and achieve financial goals. For instance, Wells Fargo & Company, a community-based financial services provider, has added AI enhancement to mobile Apps for analyzing account information, which can help them offer personalized guidance and support financial decision-making. An increase in such AI applications in mobile banking apps is expected to provide lucrative opportunities for predictive analytics in banking market growth.

Regional Analysis

North america dominates the global market

Region-wise, the global predictive analytics in the banking market is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

North America is the most significant global predictive analytics in banking market shareholder and is estimated to exhibit a CAGR of 17.81% over the forecast period. Banks and financial institutions are establishing partnerships with advanced analytics tool providers that offer innovative payment solutions using machine learning and predictive analytics. For instance, in 2016, Citigroup Inc. announced a partnership with Feedzai, one of the key players in Artificial intelligence (AI) for real-time risk management across banking and commerce, which enabled banks to make payments efficiently and securely across the globe. This is considered a major driver for predictive analytics in banking market growth in North America. In addition, various banks are adopting advanced analytics to analyze personal accounts to deliver personalized insights in different areas, such as spending habits, cash flow, and saving, which helps customer management and retention.

Additionally, various stringent regulatory compliances imposed by the government in North America for data safety and security have increased the demand for predictive analytics software in the financial industry. For instance, in 2019, the government of North America imposed the Gramm Leach Bliley ACT (GLBA) on various banks and financial institutions, which governs the protection of customers' personal information and notifies customers when data is exposed to an unauthorized person.

Europe is estimated to exhibit a CAGR of 21.1% over the forecast period. European financial institutions and banks have established partnerships with advanced analytics solution providers to improve operational management and critical decision-making and enhance customer experience. For instance, HSBC Holdings plc. partnered with Tresata in December 2018 to better understand the process, people, and product data more precisely through their AI-based software. More such partnerships are expected to create opportunities for predictive analytics in the European banking market. Furthermore, banks and financial institutions have been adopting digitization at a faster rate, owing to which there has been an upsurge in identity theft, cyber-attacks, data theft, and other business-related risks. Such a rise in the number of crimes leads to the increased adoption of predictive analytics software among banks in this region.

Furthermore, the rising need to offer enhanced financial services, identify fund spending behaviors of customers, and tackle millions of credit card transactions across the region have been driving the market growth. Furthermore, many banks in European nations are adopting predictive analytics to increase the retention rate of their customers and help financial institutions to reduce loan default and credit card risks. Such adoptions are expected to boost predictive analytics in banking market growth over the forecast period.

In the upcoming years, Asia-Pacific, currently regarded as an emerging market, is anticipated to hold most of the market share. Some of the key nations driving the expansion of the region's predictive analytics in the banking industry are China, India, and Japan. The Asia-Pacific-based banks and financial institutions have been adopting predictive analytics solutions to analyze customer behavior and prevent online fraud. Moreover, many banks and financial institutions in Singapore have adopted advanced technology to reduce the time required in the lending process and provide better customer service. For example, according to a survey completed by Standard Chartered Bank in Singapore in 2018, many banking and financial institutions have adopted unstable technology that helps speed up the SME loan lending process and reduces time in the KYC process and credit documentation. Additionally, several financial institutions in Asia-Pacific are implementing predictive analytics to boost their revenue and strengthen their ability to make decisions. Predictive analytics in banking is highly fragmented in the Asia-Pacific region, owing to many market players indulging in various development such as partnership, acquisition, and collaboration.

Segmental Analysis

The global predictive analytics in the banking market is segmented based on deployment mode, component, organization size, and application. 

Based on components, the global predictive analytics in the banking market is bifurcated into solutions and services. 

The solution segment dominates the global market and is estimated to exhibit a CAGR of 19.6% during the forecast period. Banks have adopted several new technologies, like ML and predictive analytics for fingerprint recognition and real-time fraud detection. Significant increases in external fraud, such as cyber-attack, card not present fraud, and identity theft and account takeover frauds, are being resolved by predictive analytics. For instance, according to several studies, online frauds increased by 61% in 2018 compared to 2017, and losses caused to these frauds increased by 59%. These meaningful activities have driven the adoption of predictive analytics solutions, driving the growth of predictive analytics in the banking market. Similarly, predictive analytics solutions have been helping banks to identify customers' money spending categories, and cash flow trends, which helps them maintain enhanced customer relationships, which boosts predictive analytics in banking.

Based on the deployment model, the global predictive analytics in the banking market is divided into on-premise and cloud. 

The on-premise segment owns the largest market share and is estimated to exhibit a CAGR of 19.2% during the forecast period. On-premise-based predictive analytics software has been making faster delivery of predictive insights by reducing errors through automated techniques and quality measures. In addition, the on-premises predictive deployment mode is considered widely useful in banks and financial institutions, as it involves significant investment to implement, and organizations need to purchase predictive software to manage the system and analyze historical data patterns to predict future outcomes.

Based on organization size, the global predictive analytics in the banking market is categorized into large and small and medium enterprises.

The large enterprise segment dominates the global market and is estimated to grow at a CAGR of 18.9% during the forecast period. The increase in anti-money laundering and credit card fraud among large-sized banks has motivated them to use predictive analytics technology to sustain in the competitive industry. For instance, according to several studies, around 36% of large Indian financial institutions invested in predictive analytics technology in 2018, owing to the growing number of card frauds. In addition, large banks and financial institutions have increased their focus on risk analysis, especially to effectively identify, assess, and manage risk, driving the adoption of predictive analytics solutions among financial institutions.

Based on application, the global predictive analytics in the banking market is classified into fraud detection and prevention, customer management, sales & marketing, workforce management, and others.

The customer management segment is the highest contributor to the market and is estimated to exhibit a CAGR of 17.71% during the forecast period. The surge in customer management in banking, owing to growth in lead conversion, increased productivity, and more efficient communication, has been driving predictive analytics in the banking market for customer management. Predictive analytics offers faster service, helps make informed decisions, and analyzes recent customer interactions using historical data. These influential factors drive the adoption of predictive analytics solutions for customer management and propel market growth. According to a survey conducted by expert systems in 2018, 44% of financial institutions and banking institutions have adopted predictive analytic software to increase their customer retention rate.

Recent Developments

• January 2024 - KlariVis launched a new data-driven product called Report Builder. This tool allows banking professionals to create customized charts, trends, and reports by combining different data elements. It is designed to be user-friendly, offering features such as easy export options and automatic daily updates for real-time insights.
  Additionally, KlariVis introduced Officer KPI & Goal Scorecards for real-time performance visibility, a Profitability solution, a Credit Concentration Policy Status Report, and a Peer Insights feature. These innovations aim to simplify data analytics for community banks and credit unions.
• April 2024 - Zand Bank (Zand), the first digital-only bank in the UAE, and Infosys Finacle, a division of EdgeVerve Systems, a wholly-owned subsidiary of Infosys, announced the bank's decision to use the Infosys Finacle Solutions suite to power its corporate banking services. Zand's dedication to delivering a customer-focused, future-ready banking experience supported by the most recent advancements in artificial intelligence and predictive analytics is demonstrated by the implementation of Infosys Finacle's cutting-edge cloud-native solutions on Microsoft Azure platforms.