At 20.80% CAGR, Predictive Analytics in Banking Market Anticipated to Grow $5.43 Billion By 2026 | In-Depth Analysis of Top Impacting Factors
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Predictive Analytics is considered a powerful technology of advance analytics used to track solutions for customer segmentation, risk assessment and future trend. The predictive analytics uses various kinds of techniques such as statistics, data mining, data modeling, machine learning, and artificial intelligence for predicting the emerging opportunities and identifying the business trends among organizations.
In addition, the banking and financial industry has been utilizing predictive analytics technology to track customer behavior and identify issues by analyzing unstructured data of customer emails, survey responses, banker notes, and call center transcripts, which helps them in improving business operations.
A significant increase in fraudulent activities such as accounting fraud, money laundering and payment card fraud, is the major factor driving the global predictive analytics in banking market growth. In addition, 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 predictive analytics in banking market. Conversely, integration of artificial intelligence in mobile banking apps and a rise in demand from developing economies are expected to provide major opportunities for the growth of the predictive analytics in banking predictive analytics in banking market during the forecast period.
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By application, the global predictive analytics in banking market was led by the customer management segment in 2018 and is projected to maintain its dominance during the forecast period. The major factors that drive the adoption of predictive analytics in banking solution in customer management include growth in lead conversion, increased productivity and more efficient communication have been driving the predictive analytics in banking market for customer management.
However, the fraud detection & prevention is expected to grow at a highest rate during the forecast period, owing to increase in the number of transactions in various banking and financial institution.
Region wise, the predictive analytics in banking market was dominated by North America in 2019. The major factors that drive the growth of the predictive analytics in banking market in this region owing to various banks are adopting advanced analytics, to analyze personal accounts for delivering personalized insights in different areas such as spending habits, managing cash flow and saving, which helps in customer management and retention.
However, Asia-Pacific is expected to witness the highest growth rate during the forecast period due many banking and financial institutions have adopted instabase technology that helps in speeding up the SMEs loan lending process and reduces time in KYC process & credit documentation.
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Key Findings of the Study:
1. By component, the solution segment led the predictive analytics in banking market size in terms of revenue in 2018.
2. By deployment model, the on-premise segment accounted for the highest predictive analytics in banking market share in 2018.
3. Region wise, North America generated the highest revenue in 2018.
4. Depending on the application, the fraud detection & prevention segment is anticipated to exhibit substantial growth during the forecast period.
The key players profiled in the 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 and TIBCO Software, Inc. These players have adopted various strategies to increase their market penetration and strengthen their position in the predictive analytics in banking market industry.
Chapter 1: Introduction
Chapter 2: Executive Summary
Chapter 3: Market Landscape
Chapter 4: Predictive Analytics in Banking Market By Component
Chapter 5: Predictive Analytics in Banking Market By Deployment Model
Chapter 6: Predictive Analytics in Banking Market By Organization Size
Chapter 7: Predictive Analytics in Banking Market By Application
Chapter 8: Predictive Analytics in Banking Market By Region
Chapter 9: Company Profiles
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