Predictive Analytics in Banking Market Size, Share & Analysis

Marketing and Customer Experience

Embrace the power of Advanced Predictive Analytics to provide differentiated and personalized customer experience. Use a holistic analytical marketing approach and a comprehensive CRM strategy that will support decision making, optimization and automation across different marketing activities and CRM operations in financial institutions.

  • Use Enterprise Data to leverage customer intelligence and personalize customers banking experience and satisfaction.
  • Reveal customer insights to identify new marketing opportunities and effectively address customer needs in real-time.
  • Develop financial products or services tailored to banking behaviors.

Fraud Detection, Investigation and AML

Money launderers and fraudsters continue to work night and day, shifting to channels offering the greatest opportunities:

  • Move beyond rigid, historical, rule based detection approaches to analytics approaches that learn from the data, to identify high risk transactions in real time.
  • Utilise law enforcement and national security agency grade technology to investigate cases

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7.Better cash/liquidity planning

Predictive analytics can help banks track the past usage patterns and the daily coordination between the in- and out-payments at their branches and ATM’s, hence predicting the future needs of their potential customers. Optimal management of liquid assets can result in their extra income and a proper analytics plan can help obtain an overview of future changes in investment and liquidity options.

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.

Key Market Segments

  • By Component
  • By Deployment Model
    • On-Premise
    • Cloud
  • By Organization Size
    • Large Enterprises
    • SME’s
  • By Application
    • Fraud Detection & Prevention
    • Customer Management
    • Sales & Marketing
    • Workforce Management
    • Others
  • By Region
    • North America
      • U.S.
      • Canada
    • Europe
      • UK
      • Germany
      • France
      • Italy
      • Spain
      • Russia
    • Asia-Pacific
      • China
      • India
      • Japan
      • Singapore
      • South Korea
      • Rest of Asia-Pacific
    • LAMEA
      • Latin America
      • Middle East
      • Africa

Key Market Players

  • SAP SE


Efficient cross-selling of products can happen by analyzing the existing customer behavior at places where multiple products are offered. Which specific products are to be sold to whom hence predicting the outcome is what successful cross-sellers do. And all of this results in more effective cross-selling thus increasing profitability and strengthening the customer relationship. Today, securing one profitable customer is a big task for banks, hence cross-selling another product to an existing customer helps a lot.

Predictive analytics helps examine customers’ usage, spending, and other behavior and leads to effective cross-selling of the right product at the right time. 

Case Studies

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3. Transforming customer service

Customers are the lifeblood of most businesses, banks included. This is enough reason to invest in innovation and digital enhancements to continually improve customer experience, but it can also be considered as a way to reduce churn and differentiate your bank from other financial institutions.

After all, 72% of banking customers who had a negative customer service experience either engaged less or switched banks altogether, according to Cisco.

Incorporating data analytics in banking can greatly enhance your organization’s customer service. You can use machine learning to provide the right information at the right time, utilize chatbots to provide timely responses, and employ predictive modeling to provide personalized experiences.

And of course, all of this would be easier with an integrated view of data (that means no more data silos!). Providing quality customer service requires a 360-degree view of the customer, possible with analytical platforms like Analance that has capabilities built-in from BI to AI.

Predictive Analytics in Financial Services

As noted earlier, there are over 2.5 quintillion bytes of data generated every day. As a way to learn what their customers want and better their service delivery, businesses are now taking the time to analyze consumer data. This data enables a business to learn where consumers spend the most time and associate shopping behaviors. For instance, every time a customer carries out a transaction, the bank collects some data and uses predictive analytics to gain more insight on the customer’s banking behavior. This, in turn, enables the bank to create solutions that are in perfect sync with what that customer may need. As a result, the banking experience gets better with each transaction. Here’re a few applications of predictive analytics in financial services

Transactional Analysis

Transactional analysis within a financial institution often includes the application of big data techniques, or data mining, to improve how banks segment, target, acquire, and retain customers. With advanced large-scale transactional analysis, financial institutions can personalize marketing to a particular customer by understanding which transactional behaviors may trend towards a specific life event. Transactional behavior can help identify customers who may be interested in a new auto loan, help with college tuition, retirement investments, or mortgage refinancing. This insight enables banks to focus their sales and marketing activities to the right customer at the right time. In the past, this type of transactional analysis would take ages. Thanks to new artificial intelligence and machine learning technologies that power predictive analytics, financial institutions can analyze this type of financial data within seconds.

Fraud Detection

Fraud detection is yet another common application of predictive analysis in financial services. As noted earlier, predictive analysis uses data, statistical algorithms, and machine learning to forecast future outcomes. In the case of fraud detection, financial institutions apply machine learning techniques to find inaccurate credit predictions and fraudulent transactions done online and offline. Other applications of predictive analytics in financial services include:

  • Personalized marketing
  • Customer spending patterns
  • Lifetime value prediction
  • Transaction channel identification
  • Realtime and predictive analytics



Further to the concept of utilizing large customer data sets, AI is paramount in developing successful marketing strategies. In recent years, open banking protocols have helped financial institutions to share data and facilitate the ‘big data’ revolution.

However, examining this data in a constructive manner can be incredibly time-consuming, so the need for AI is clear. The systems do not simply scan the data and compile informative spreadsheets: AI can actively identify changes and patterns to formulate novel approaches to marketing opportunities.

To drive new customer acquisition, banks can utilize these features to automate the clustering of potential leads into interest-specific groups. With analytical tools like response modeling, AI-enhanced systems can develop personalized and targeted marketing campaigns with high success rates. 


AI and predictive analytics not only offer a range of applications for the banking sector but represent an integral part of the financial industry as a whole. With a growing knowledge of technology and what it has made possible, customer expectations are now higher than ever.

Going forward, it is highly unlikely any serious contender in the financial world will survive without a well-designed strategy for the implementation of AI and predictive analytics.

Disclaimer:  The author of this text, Robin Trehan, has an undergraduate degree in Economics, Masters in international business and finance, and MBA in electronic business. Trehan is Senior VP at Deltec International The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries, and/or employees.

How Bank Customers Benefit

Predictive analytics can improve your experience as a customer in several ways. That said, some may find it unsettling that financial institutions have so much information, and that they depend on computers to make decisions that affect your life. On the bright side, computers are always available, and they don’t discriminate against customers they don’t like (assuming the model is built to avoid bias).

Credit Scoring

You may already be familiar with predictive analytics—credit scoring models use data to predict your creditworthiness. For example, the FICO credit score uses statistical analysis to predict your behavior, such as how likely you are to miss payments. Your score is based, in part, on how borrowers similar to you have performed in the past.

Help With Budgeting

Computer models can help you manage your finances. They can identify when income and expenses typically hit your account, and they can see where your money goes. As a result, they may be able to prevent problems. For example, if your mortgage payment hits your account on the 15th of every month but you’re running low on cash, your bank can send an alert.

Using analytics, software can alert you so you can transfer funds from other accounts or contact your mortgage servicer so you avoid overdraft charges, late payment penalties, and other problems.

Fraud Prevention

Sometimes identity theft is entirely out of your control. Even if you’re extremely careful, thieves can steal your information in data breaches and use your card number or other sensitive details. Banks with predictive analytics are better equipped to spot problems. They may notice when somebody else uses your credit card or if somebody logs in to your account in an unexpected way. They may also be able to reduce bad check scams, which can cause significant losses for victims, by analyzing data patterns.

Financial Management

Software can assist with bigger-picture decisions as well. For example, after reviewing your finances, an intelligent program can determine whether or not it makes sense to make extra payments on loans, and how much you might be able to put toward eliminating your debt or add to savings. Banks might also be able to coach you on how to earn higher rates on your savings.

Loan Approval

Lenders are getting more sophisticated about how they evaluate loan applications. They realize that not everybody has a high FICO score—but they should still qualify for loans. Some people have never established credit, and others are still good borrowers, even with a few negative items in their credit reports. An internal Equifax study showed that some lenders unnecessarily deny loans due to outdated loan underwriting criteria, but artificial intelligence may help nontraditional borrowers get approved.

Prescriptive Analytics for Trading Intelligence

The difference between predictive and prescriptive analytics is mainly that prescriptive analytics takes the technology a step farther to recommend the next best course of action. Once the software finds all viable next steps for the user, it recommends one with the highest likelihood of success. Often, predictive analytics will simply allow the user to more cleanly plug different variables into situations they need to have information on before they can make a decision.

Predictive analytics software correlates the goal of the data science experiment with data points that have lead to similar results to that goal in the past.

For example, if a data scientist wanted to test the best way to improve ROI on changes to their customer smartphone app, the system would correlate popular app updates with ROI. The data scientist would then be able to see which updates to the mobile banking app elicited the most customer satisfaction.

Banks could use trading insight found using prescriptive analytics to help their clients who buy and sell stocks make more informed decisions.

For banking customers, this information could be channeled into a mobile banking app and delivered through a section about stocks and trading. Alternatively, they could use this intelligence internally to have a more detailed image of the banking stock market and further understand what is leading people to buy stock in their company.

We spoke to Ann Miura-ko, co-founder and partner at Floodgate, about how prescriptive analytics software could benefit financial institutions by being “self-driving.” In this case, she refers to the software always determining the next probability as new data enters its purview. When asked if prescriptive analytics software could be used to recommend business operations to various departments throughout every process, Miura-ko said:

My belief is that the data actually already exists out there in terms of how all of this information ought to be tied together, so when I talk about probabilistic inputs, it’s not just around things we’re never certain about…there’s also things about the future that we should be able to predict and we should know that there’s some sort of newsworthy event that then is going to have trickle-down effects upon my business.

It is clear from this quote that the possibilities of prescriptive analytics within the enterprise may be vast. It is important to recognize the amount of automation already possible with prescriptive analytics, as companies may continue to innovate on it for the banking space.

Our research did not yield any results showing a bank’s success with a vendor’s software for trading intelligence. Because of this we can infer that the landscape of applications for trading and stock intelligence may be relatively nascent compared to other banking solutions.

This could be indicative of major banks prioritizing innovation outside of this type of intelligence. Other, possibly more important areas for innovation include loan and credit intelligence, fraud detection, and prevention.

Header Image Credit: Admiral Markets