Banks and credit unions are not only repositories of money, but of business and consumer data. Predictive analytics tools help your organization more effectively use your data to enhance business decisions, gain competitive advantages, improve satisfaction and fortify user loyalty by personalizing their experiences. Regarding user satisfaction, McKinsey indicates that U.S. retail banking end users’ deposits grew 84% faster at banks with the highest degree of user satisfaction versus those with the lowest satisfaction ratings.
As far as the vast amounts of data stored at banks and credit unions, consider just the information collected in your financial institution’s Know Your Customer (KYC) compliance and onboarding process. Each user will generate plenty of data that can help with the potential personalization of their experiences as well as the risk mitigation and management of any bad actors.
Data regulation and predictive analytics are a good match. According to Fintech Futures, “IT leaders appear to recognize this opportunity, with nearly three quarters (72%) believing the use of predictive analytics in business intelligence platforms can help financial services organizations comply with data regulatory frameworks.”
What Are Predictive Analytics?
Predictive Analytics is a mix of user data, probabilities and artificial intelligence. It uses past data and trends to predict future user behavior with its primary goal to identify historical trends to forecast future ones. It incorporates data mining, AI and machine learning algorithms to handle the massive amounts of user data to make them more manageable and actionable. These higher probability projections can be used to enhance business decisions because they are replicable, data-based and don’t rely solely on “gut instinct.”
Predictive analytics work because of the sheer size of data used to generate these trends or projections. The more data your bank or credit union has as a foundation, the more accurate your organization’s decisions can be. The analytics need not be limited to just basic information like account balances and demographics — it can also be augmented by consumer sentiment. For example, using purpose-built surveys, social media polls and service calls can help answer what your user wants in the future and what they want right now.
3 Types of Predictive Analytics Tools
Effectively, predictive analytics can be done manually or with AI and machine learning. However, both use your previous data to make assumptions or predictions about your users’ future behaviors. The key question that predictive analytics tools attempt to answer is: What might happen next? Here are three general types of predictive analytics forecasting tools:
By creating mathematical equations using two variables, sometimes called linear regression, you can determine certain relationships that predict outcomes if one variable is changed. If more than two variables are used, then it’s called multiple regression. Regression analysis is often used for trend forecasting. “Regression allows us to gain insights into the structure of that relationship and provides measures of how well the data fit that relationship,” explains Harvard Business School Professor Jan Hammond
- Example one: In a simple form, regression analysis takes past data and attempts to form a trend.
- Example two: In a mathematics form (linear regression), Y= a+b*X, it has one estimated dependent variable score, “y,” the constant is “c,” the regression coefficient is “b” and the independent variable is “x.”
This type of predictive analysis uses AI and machine learning to recognize patterns and perform tasks based on algorithms created around those patterns. Artificial Intelligence processes data with machine learning, sometimes called deep learning, to create a system where computers learn from their mistakes and adapt continuously at rapid speed. Neural networks are often used for recognizing faces and biometrics.
In a multi-university paper, including the University of York (UK), Military University of Technology (Poland), et al., it was shown that it was possible to authenticate fingerprints using a type of deep learning: Convolutional Neural Network. Interestingly, when tested, there were only 10 failures out of 5,000 images, a success rate of 99.87%.
Decision Tree Analytics
Decision trees are similar to actionable flowcharts that model potential scenarios, costs, uses and consequences. Decision trees are useful for non-numerical data points and sets. There are two primary types of decision trees:
- Categorical variable decision trees. This method uses if/then style thinking. Each stage of the decision process will fall into one category or the other (e.g., yes or no) with no gray area.
- Continuous variable decision trees. This method might be useful if the variables are unknown. Ex: the decision tree may be able to predict your user’s income based on available information such as their line of work, age, credit score and other continuous variables.
The Benefits of Predictive Analytics
Predictive analytics tools can be indispensable when paired with your dynamic digital banking solutions platform. These tools are especially adept at processing vast amounts of data — much faster than any humans could do alone. According to CIO Insight, “Predictive tools enable company leaders to move away from gut instinct and assumptions.” Another study showed 63 percent of businesses cited their predictive analytics tools as a competitive advantage, according to IBM.
Fraud Detection, Mitigation and Prevention
In 2021, global fraud losses reached a staggering 6.4% of GDP, or $5.38 trillion, according to the University of Portsmouth. Predictive analytics can help by using your big data to identify and isolate suspicious accounts or transactions. Robust machine learning algorithms can detect minute patterns of fraudulent behavior that would otherwise go unnoticed by human counterparts. Even the best team of fraud experts fall short when it comes to chasing fraudulent behavior in real-time and at-scale. Predictive analytics can help rebalance the scales. The best part is the fraud detection algorithms are on all-day and all-night, 24/7.
Enhancing End-User Experiences
Predictive analytics are purpose-built to seek out patterns or behaviors out of character. For example, if your user’s monthly transactions follow a certain pattern or cadence and something happens out of the ordinary, your analytics tools could alert them. More specifically, if authorized by the user, then the system could automatically transfer funds between accounts if they are running a low balance, therefore avoiding a late fee and preventing ill will or fee disputes.
According to a McKinsey report, in Europe, those banks that have replaced legacy systems with AI and machine learning digital banking solutions have experienced multiple benefits:
- 10% increase in sales of new products
- 20% savings in capital expenditures
- 20% increase in cash collections
- 20% declines in user churn
In short, AI, machine learning and predictive analytics can boost your revenues, lower costs via automation efficiencies and uncover additional opportunities within your organization and your users.
How To Upgrade Your Legacy Systems With Predictive Analytics
Worldwide Artificial Intelligence spend will reach $36.8 billion worldwide by 2025, according to a report by Tractica. That’s up from $643 million in 2016, an increase of roughly 5,723% in less than 10 years.
“Likewise, a few key industry sectors including consumer products, business services, advertising, finance & investment, media & entertainment and defense applications will drive significant revenue for AI software implementations in addition to AI-driven hardware and service sales, but during the coming decade the technologies will have an effect on almost every conceivable industry sector,” says Aditya Kaul, research director at Tractica. McKinsey estimates AI technologies could potentially deliver up to $1 trillion of additional value each year to the global banking sector.
Your bank or credit union can implement predictive analytics technology across multiple departments: risk management, cross-selling, upselling, credit applications, underwriting, KYC/AML compliance and marketing. Most importantly, it will strengthen your user’s experience through personalized and potential real-time solutions. Ultimately, generating a stronger bond between the user and your organization, while reducing costs.
AI, machine learning and predictive analytics tools may seem a bit too futuristic. However, if your organization feels that way, it’s likely any number of your competitors feel the same way, which is precisely why the time to act is now, not later. Seek out the right strategic partner to upgrade your legacy systems and enjoy a competitive edge before it becomes as commonplace as the ATM.
Marty Aquino has been a passionate writer on venture capital, technology, forecasting, risk mitigation, wealth and entrepreneurial topics since 2009. He is the founder of Carbonwolf Energy, a venture-capital firm specializing in world-changing and status-quo-defying technologies and people.
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IBM – Predictive Analytics
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Medium – Regression for Predictive Analytics
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Corporate Finance Institute (CFI) – Know Your Client (KYC)
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KYC 2020 See Far – Solving The False Positives Paradox in Sanctions Screening
Harvard Business School – What Is Predictive Analytics? 5 Examples
Statistics How To – Linear Regression: Simple Steps, Video. Find Equation, Coefficient, Slope
Massachusetts Institute of Technology (MIT) – These neural networks know what they’re doing
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Corporate Finance Institute (CFI) – Decision Tree
Crowe/University of Portsmouth – The financial cost of fraud 2021
McKinsey & Company – An executive’s guide to machine learning