New York, United States, Nov. 28, 2023 (GLOBE NEWSWIRE) -- Predictive analytics is an advanced analytics technology that determines current trends in organizations and manages their financial risks using historical and current data. Various techniques, including statistics, data mining, data modeling, machine learning, and artificial intelligence, are widely used in predictive analytics to identify financial uncertainty, catastrophes, strategic management errors, and legal liabilities. Analyzing unstructured data collected from customer emails, survey responses, banker notes, and call transcripts, banks and financial institutions frequently employ predictive analytics techniques to monitor customer behavior and identify emergent issues. It assists banks and other financial institutions develop a customer experience strategy to enhance their communications and banking services.
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Increasing Adoption of Advanced Technologies for Fraud Detection Drives the Global Market
According to Straits Research, "The global predictive analytics in the banking market was valued at USD 2,550.22 million in 2022 and is projected to reach USD 13,760.21 million by 2031, registering a CAGR of 20.6% during the projected period (2023–2031)." In recent years, a significant increase in fraudulent activities has been observed in banking and financial institutions. Customers have begun utilizing banking services via multiple channels, increasing bank forgeries such as money laundering, credit card fraud, and fraudulent loans. However, sophisticated technologies such as predictive analytics and machine learning algorithm-based fraud detection solutions can reduce fraudulent activities. Fraud detection based on machine learning enables banks to detect online fraud and rapidly recommend the necessary actions to decision-makers.
Several large institutions have begun utilizing fraud detection software based on predictive analytics to detect fraudulent activities across all channels involved in payment processing. For instance, Danske Bank has implemented Teradata's fraud detection solution, which combines machine learning and AI algorithms. The solution assisted Danske Bank in detecting 50% more real-time deception. Therefore, an increasing number of such implementations of predictive analytics for fraud detection by banking and financial institutions have been driving the expansion of predictive analytics in the banking market.
Integration of Artificial Intelligence in Mobile Apps Creates Tremendous Opportunities
Incorporating sophisticated technologies such as AI into mobile banking applications has enabled customers to analyze account information and receive personalized financial advice. In addition, these AI-powered mobile banking applications have enhanced the ability of financial institutions to increase customers' financial wealth, gain a more comprehensive view of their finances, and attain financial objectives. Wells Fargo & Company, a community-based financial services provider, has added AI-enhanced mobile applications for analyzing account information, enabling them to provide personalized guidance and facilitate financial decision-making. An increase in such AI applications in mobile banking apps is anticipated to create lucrative opportunities for expanding the market for predictive analytics in banking.
Regional Analysis
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 forming alliances with providers of advanced analytics tools that offer innovative payment solutions based on machine learning and predictive analytics. In 2016, Citigroup Inc. announced a partnership with Feedzai, one of the leading Artificial intelligence (AI) companies for real-time risk management across banking and commerce. This collaboration enabled banks to make efficient and secure global payments. Several banks are also employing advanced analytics to analyze customer accounts to provide personalized insights regarding spending patterns, cash flow, and savings, which aids in customer management and retention. In addition, numerous 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 sector. For example, in 2019, the government of North America imposed on various banks and financial institutions the Gramm Leach Bliley Act (GLBA), which regulates the protection of customers' personal information and notifies customers when data is exposed to an unauthorized party.
Europe is estimated to exhibit a CAGR of 21.1% over the forecast period. Various European financial institutions and banks partnered with advanced analytics solution providers to enhance operational management, critical decision-making, and the customer experience. For example, HSBC Holdings plc. partnered with Tresata to better understand our process, people, and product data using their AI software. In the European banking market, more such partnerships are anticipated to generate opportunities for predictive analytics. Moreover, banks and financial institutions have accelerated their digitization adoption, increasing identity theft, cyberattacks, data theft, and other business-related hazards. This region's institutions are increasingly adopting predictive analytics software because of increased crime. In addition, the increasing demand for enhanced financial services, identifying customer purchasing patterns, and managing millions of credit card transactions in the region have been driving market growth.
Key Highlights
- 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.
- 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.
- 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.
- 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.
- 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.
Competitive Players
The prominent players in global predictive analytics in banking market analysis are Alteryx, Inc., Fair Isaac Corporation, IBM Corporation, Microsoft Corporation, Oracle Corporation, SAP SE, SAS Institute, Inc., Tableau Software, Inc., Teradata Corporation, TIBCO Software, Inc.
Market News
- In May 2023, Alteryx, Inc., the Analytics Cloud Platform company, announced the launch of the Alteryx Maveryx Community at the annual user conference, Inspire. The Alteryx Maveryx Community encapsulates all customers, community members, and employees that share the maverick's mindset of exploring the unknown and not settling for the status quo.
- In May 2023, Alteryx, Inc., the Analytics Cloud Platform company, announced expanded cloud-connected platform experiences for its flagship Alteryx Designer product with the Alteryx Analytics Cloud Platform and new, upcoming cloud-based Location Intelligence capabilities to bring spatial analytics to the masses. The unified, self-service and enterprise-ready Alteryx Analytics Cloud Platform now bridges data across all systems with increased governance for faster insights.
Global Predictive Analytics in Banking Market: Segmentation
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
By Regions
- North America
- Europe
- Asia-Pacific
- LAMEA
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