Predictive Analytics in Banking Market

Predictive Analytics in Banking Market Outlook - 2026

The global predictive analytics in banking market size was valued at $1.20 billion in 2018, and is projected to reach $5.43 billion by 2026, registering a CAGR of 20.80% from 2019 to 2026. Predictive analytics is an advanced analytics technology that identifies the current trend of organizations and controls the financial risks for the organization using historical data and current data. 

The predictive analytics uses a wide variety of 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. 

The predictive analytics in banking market trends include internet of Things (IoT) has been one of the most useful innovations in the last few decades, leading to introduction of billions of IoT-based devices across the globe, which is drive the growth of the market. In addition, significant increase in fraudulent activities such as accounting fraud, money laundering and payment card fraud has been the major factor driving the global predictive analytics in banking market growth. 

Furthermore, predictive analytics have been helping the bank and financial institutions to predict their incoming and outgoing property payment and customer flow, which is driving the growth of the market. However, issues associated with implementation and integration among banks and financial institutions hamper the growth of the market. Conversely, a rise in demand from developing economies and integration of artificial intelligence in mobile banking apps are expected to provide major opportunities for the growth of predictive analytics in the banking market during the forecast period.

The solution segment is expected to garner significant share owing to banks identify customers' money spending categories, and cash flow trends, which helps them in maintaining enhanced customer relationships. This is an important factor that boosts predictive analytics in banking. However, the service segment is expected to grow at a highest rate during the forecast period, owing surge in demand for on premises management services for predictive analytics during the forecast period. Thus, driving the growth of predictive analytics in banking market in this segment.

The report focuses on the growth prospects, restraints, and trends of the predictive analytics in banking market analysis. The study provides Porter’s five forces analysis to understand the impact of various factors such as bargaining power of suppliers, competitive intensity of competitors, threat of new entrants, threat of substitutes, and bargaining power of buyers on the predictive analytics in banking market.

Segmentation

The global predictive analytics in banking market is segmented on the basis of component, deployment model, organization size, application, and region. In terms of component, it is bifurcated into solution and services. Based on deployment model, the market is segmented into on-premise and cloud. As per organization size, it is divided into large enterprises and SMEs.

In terms of application, the market is segmented into fraud detection & prevention, customer management, sales & marketing, workforce management and others. Region wise, it is analyzed across North America, Europe, Asia-Pacific, and LAMEA.

Top Impacting Factors

Increase in Adoption of Advanced Technologies For Fraud Detection 

Significant rise in fraudulent activities has been observed in banking and financial institution in the recent years. The customers have started using banking services across a variety of channels leading to increased number of bank frauds such as money laundering, payment card fraud, and fraudulent loans. However, such fraudulent activities can be reduced by advanced technologies, such as predictive analytics, and machine learning algorithm-based fraud detection solution. 

The machine learning-based fraud detection help to banks for online fraud detection and quickly suggests 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.

Surge in Number of Risk Management Function 

Risk management has been one of the most challenging functions of banking institutions since 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 risk, operational risk, credit risk, operational risk, and others. 
The banking industry generates a huge volume of data on a daily basis that can be used by predictive analytics to develop the number of risk functions such as internal audit, stress testing, bank failure prediction and operational & liquidity risks. In addition, predictive analytics use in the banks supports initial discovery of high-risk accounts to reduce fraudulent and default cases.

Competitive Analysis

The key players profiled in the predictive analytics in banking industry analysis are Alteryx Inc., Fair Isaac Corporation, IBM Corporation, Microsoft corporation, Oracle Corporation, SAP SE, SAS Institute Inc., Tableau Software Inc., Teradata Corporation and TIBCO Software Inc. These key players have adopted various strategies such as product portfolio expansion, mergers & acquisitions, agreements, geographical expansion, and collaborations to increase their market penetration and strengthen their position in the industry. 

Key Benefits for Stakeholders:

• The study provides an in-depth analysis of the global predictive analytics in banking market forecast along with the current & future trends to elucidate the imminent investment pockets.
• Information about key drivers, restraints, and opportunities and their impact analysis on the global predictive analytics in banking market size is provided in the report.
• Porter’s five forces analysis illustrates the potency of the buyers and suppliers operating in the industry.
• The quantitative analysis of the predictive analytics in banking market share for the period 2019–2027 is provided to determine the market potential.

 Predictive Analytics in Banking Market Report Highlights