Financial Predictive Analytics Statistics, Trends And Facts 2023

Financial predictive analytics is the use of statistical algorithms and machine learning techniques to analyze financial data and predict future outcomes. This is becoming increasingly important in the financial industry as companies strive to stay ahead of their competition and make informed decisions.

In this blog post, we will explore financial predictive analytics statistics to help you understand the importance and impact of this field.

Key Financial Predictive Analytics Statistics 2023 – MY Choice

In 2020, the global market for predictive analytics was valued at $4.5 billion, and it is projected to grow to $12.4 billion by 2025.
According to a survey by Deloitte, 86% of financial services companies have already implemented or are planning to implement predictive analytics within the next two years.
The financial sector is the largest user of predictive analytics, accounting for 28% of its total usage across all industries.
The most commonly used predictive analytics techniques in finance include regression analysis (used by 84% of companies), decision trees (69%), and clustering (59%).
In the banking industry, predictive analytics is used for fraud detection, risk management, and customer segmentation. A study by McKinsey found that banks that use predictive analytics for these purposes can improve their ROI by up to 20%.
In the insurance industry, predictive analytics is used for underwriting, claims management, and fraud detection. A study by Accenture found that insurers that use predictive analytics can reduce claims processing time by up to 30%.
Predictive analytics is also used in investment management, where it is used to identify profitable trades, forecast market trends, and manage risk. A study by State Street Global Advisors found that using predictive analytics can improve investment returns by up to 50 basis points.
In retail banking, predictive analytics is used for customer retention and cross-selling. A study by Oracle found that banks that use predictive analytics for customer retention can increase customer retention rates by up to 10%.
According to a study by PwC, companies that use predictive analytics are twice as likely to be in the top quartile of financial performance than those that do not use predictive analytics.
The most commonly used software for financial predictive analytics is SAS, used by 33% of companies, followed by R (21%) and Python (16%).

Financial Predictive Analytics Stats

Financial Predictive Analytics Adoption Statistics

Statistics	Data
Financial service companies’ A&BI adoption rate	29%
Companies reporting Big Data and AI adoption as a challenge	73.4%

Financial Predictive Analytics Latest Statistics

Statistics	Data
Predicted consumer confidence after COVID19	Low
Companies expected to base decisions on graph technologies by 2023	30%
Predicted savings from the use of predictive analytics	26% for 25% or more
Healthcare executives whose organizations are using or planning to use predictive analytics within the next five years	Most
Importance of predictive analytics to healthcare executives’ business future	93%
Projected growth for data analysts between 2020	15%

Examples of Financial Predictive Analytics Use

Statistics	Data
Method used by a salon owner to predict customer visits	Averaging total visits over the past 90 days
ROI gained by Staples through customer behavior analysis	137%
Reduction in warranty costs achieved by Lenovo through predictive analytics	10-15%

Big Data Statistics

Statistics	Data
Estimated global datasphere growth by 2025	175 zettabytes
Estimated value of big data industry by 2023	$77 billion
Influence of Netflix on content viewed by subscribers due to accurate data insights	80%
Predicted data growth in the financial industry in 2021	700%
Estimated percentage of unstructured and semi-structured data collected by enterprises	80%
Percentage of data collected by enterprises that is analyzed	12%
Percentage of data collected by enterprises that goes unanalyzed	88%

Other Financial Predictive Analytics Statistics

Statistics	Data
Percentage of companies with a mix of legacy and modern cloud technologies	81.7%
Employees who cannot gather insights in their required timeframe	63%
Percentage of companies with a data-driven culture	26%
Countries with the highest compound annual growth rate in financial predictive analytics	Argentina (20.8%)
Percentage of companies claiming self-service business intelligence is essential	62%
Percentage of business experts with a desire to improve their data literacy skills	71%

The global predictive analytics market size was valued at USD 7.2 billion in 2020 and is expected to reach USD 21.5 billion by 2025, growing at a CAGR of 24.5% during the forecast period (Source: MarketsandMarkets).
The banking and financial services sector is the largest user of predictive analytics, accounting for 35% of the market share (Source: Grand View Research).
The predictive analytics software market is expected to grow at a CAGR of 25.6% during the forecast period 2020-2025 (Source: Mordor Intelligence).
The global predictive analytics market is expected to reach $10.95 billion by 2027, growing at a CAGR of 21.8% from 2020 to 2027 (Source: Allied Market Research).
In a survey conducted by Deloitte, 85% of financial institutions reported that they are using predictive analytics to improve their business operations.

Financial Predictive Analytics Industry Overview

The global financial predictive analytics market is expected to reach $12.41 billion by 2023, growing at a CAGR of 21.7% from 2018 to 2023. (MarketsandMarkets)
The banking, financial services, and insurance (BFSI) sector are expected to have the largest market share in the financial predictive analytics market. (MarketsandMarkets)
North America is expected to have the largest market share in the financial predictive analytics market due to the early adoption of these technologies by businesses in the region. (MarketsandMarkets)
The use of predictive analytics in finance is expected to increase by 50% in the next five years. (Forbes)
70% of financial institutions say that predictive analytics has had a positive impact on their business. (Oracle)

Applications of Financial Predictive Analytics

Fraud detection is one of the most common applications of predictive analytics in finance. By analyzing historical data, predictive models can identify patterns of fraud and flag suspicious transactions in real-time. (SAS)
Predictive analytics can be used to optimize loan approval processes by analyzing data on credit scores, employment history, and other factors to predict the likelihood of repayment. (KPMG)
Predictive analytics can help insurance companies make more accurate predictions about risk and improve their underwriting processes. (Deloitte)
Asset management firms use predictive analytics to identify opportunities for investments and to predict future trends in financial markets. (CFA Institute)
Banks use predictive analytics to forecast demand for cash and to optimize ATM replenishment schedules. (IBM)

Benefits of Financial Predictive Analytics

Financial institutions that use predictive analytics are 2.5 times more likely to have a significant increase in revenue compared to those that do not. (Oracle)
Predictive analytics can help financial institutions reduce fraud by up to 60%. (SAS)
By using predictive analytics, businesses can reduce their customer churn rate by up to 25%. (Forbes)
Predictive analytics can help businesses reduce their customer acquisition costs by up to 23%. (KPMG)
Financial institutions that use predictive analytics can reduce their credit losses by up to 10%. (Deloitte)

Challenges in Implementing Financial Predictive Analytics

One of the biggest challenges in implementing predictive analytics is the lack of quality data. Predictive models require large amounts of data to make accurate predictions, but if the data is incomplete or inaccurate, the models will not be reliable. (KPMG)
Another challenge in implementing predictive analytics is the complexity of the models. Predictive models can be difficult to understand, and if the models are too complex, they may not be accepted by decision-makers. (Deloitte)
Financial institutions may struggle with integrating predictive analytics into their existing systems and processes. (CFA Institute)
Privacy concerns are also a challenge when implementing predictive analytics. Financial institutions must ensure that they are collecting and using customer data in a responsible and ethical manner. (SAS)
Another challenge is the cost of implementing predictive analytics. Financial institutions must invest in the technology and resources necessary to develop and maintain predictive models. (Oracle)

Financial Predictive Analytics Facts

Predictive analytics can help financial institutions in risk management, fraud detection, and customer analytics.
Predictive analytics can be used to forecast market trends and make investment decisions.
Predictive analytics can help in automating routine financial tasks, reducing costs and errors.
Predictive analytics can help in improving customer experience and retention by personalizing offerings and recommendations.
Predictive analytics can help in identifying cross-selling and up-selling opportunities.

Financial Predictive Analytics Trends

Increased adoption of cloud-based predictive analytics solutions.
Growing demand for real-time predictive analytics solutions.
Integration of predictive analytics with artificial intelligence and machine learning.
Increased use of predictive analytics in the insurance industry.
Increased use of predictive analytics in supply chain management.

Financial Predictive Analytics Adoption

Large financial institutions such as JPMorgan Chase, Bank of America, and Citigroup are using predictive analytics to improve their business operations.
Small and medium-sized financial institutions are also adopting predictive analytics to gain a competitive advantage.
Insurance companies are using predictive analytics to forecast claims and fraud detection.
Hedge funds and asset management firms are using predictive analytics to make investment decisions.
Retail banks are using predictive analytics to improve customer experience and increase sales.

Financial Predictive Analytics Market Analysis

The North American market is expected to dominate the global predictive analytics market during the forecast period, owing to the high adoption rate in the region.
The Asia Pacific region is expected to witness significant growth in the predictive analytics market during the forecast period, owing to the growing adoption of predictive analytics in the region.
The banking and financial services sector is expected to dominate the predictive analytics market during the forecast period, owing to the high adoption rate in the sector.