Banking fraud is growing in size, speed and frequency… just like the digital mediums they prey on, making it more rampant and expensive than ever. According to the ABA Banking Journal, in 2021, it cost U.S. financial institutions a staggering $4.00 for every $1.00 of fraud lost. The additional costs were incurred via fees, interest, fines, legal fees, labor and investigation costs — on top of the face value of the fraudulent transaction. Fraudsters show no sign of slowing:
- U.S. banks’ online banking fraud costs rose to 33% in 2021, up from 26% in 2020.
- U.S. banks’ mobile banking fraud costs rose to 29% in 2021, up from 20% in 2020.
- U.S. banks’ in-person fraud costs declined to 21% in 2021, down from 29% in 2020.
- Fraud losses happen throughout the customer life cycle: from new deposit account opening, mortgage, car loan or brokerage account.
- 43% of survey respondents said that the distribution of funds stage was the stage most vulnerable to fraud, followed by account login.
- New account fraud totaled nearly 302,000 incidents in 2021
- Financial institutions cited their top concern was identity verification for both online and mobile — regardless of the stage in the customer journey.
- According to CNBC, consumer fraud is up by leaps and bounds:
- Reported fraud increased more than 70% from $3.4 Billion to $5.8 Billion, according to the Federal Trade Commission (FTC).
- Nearly 2.8 million fraud reports were filed to the FTC in 2021 - a record amount dating back to 2001.
- Roughly 1 in 4 of these scams resulted in an average financial loss of $500
- Interestingly, these statistics don't include other forms of fraud like identity theft. More than 1.4 million U.S. consumers reported complaints of identity theft.
According to BioCatch in their "2021 Fraud Transformation Survey: Detecting and Preventing Emerging Schemes," a survey that polled cybersecurity and fraud professionals from over 175 financial organizations worldwide:
- 72% of the financial institutions cited account takeover as the top fraud threat.
- Other top threats cited were phishing, synthetic ID fraud and social engineering scams.
- Lack of visibility from new digital technologies was cited as a top fraud management challenge by 47% of survey respondents.
- The majority of financial institutions, 2 out of 3, plan to increase their fraud mitigation spend.
But what if you had tools to help you identify higher-risk events and transactions?
What Are Predictive Analytics Tools?
Predictive analytics tools use vast amounts of data to identify high-probability outcomes of future trends and events using data science techniques. It can use historical and/or real-time data to forecast more likely scenarios to help you make better, faster and more accurate decisions.
One of the key drivers and advantages of artificial intelligence (AI) and machine learning (ML) predictive analytics tools is their ability to organize and interpret huge amounts of data extremely quickly. Further, they do not stop at 5 p.m. to go home. They use existing data analytics models, learn from the data and continue to improve upon the models 24 hours a day, seven days a week.
3 Types of Predictive Analytics Techniques
Predictive analytics tools can incorporate one or more of the following methods:
1. Data Mining
Massive amounts of data are harvested or mined to uncover certain patterns or relationships between variables set by you or some other algorithm. After the patterns are identified, they can be used to understand previous events, which, in turn, can be used to potentially forecast similar future actions of the rightful user versus the irregular actions of a fraudster.
2. Machine Learning
ML also seeks out patterns and relationships. However, it can do so with more complex data sets and can build upon existing models. For example, ML can help prevent fraud by analyzing millions of data points, transactions and consumer behavior patterns to seek out outlying occurrences and label them as potential fraud events. ML differs from data mining by being able to customize its algorithms on an individual user basis.
3. Statistical Modeling
Statistical data models involve using vast amounts of mined data to build probability models of certain outcomes. For example, a banker could use existing customer data to predict which customers are more likely to need certain services by comparing their profile with others who have already purchased said services.
According to the U.N. Office of Drugs and Crime, an estimated $2 trillion is laundered globally in one year., or 2% to 5% of global GDP. AI and ML predictive analytics can help fight this onslaught of fraud.
Machine learning predictive analytics tools can help uncover fraud threats in real-time. Preventing fraud represents a daunting challenge to every size of bank — even mega banks. And, since the number of online transactions and digital openings are only going to continue trending upward, customers expect security as a baseline — not as an afterthought or premium add-on. Fraud is likely to increase as the world adapts post-pandemic. Now more than ever, it is imperative that your bank or credit union starts incorporating capable predictive analytics tools to detect fraudster activities and high-risk security threats.
Predicting and Halting Fraud
All predictive analytics tools are not created equal. Predictive analytics are only as good as the company behind it. The speed of the digital world is only increasing. Those organizations resisting or hesitating to adopt AI and ML predictive analytics are likely to struggle and lose customers to more agile competitors. Strong predictive analytics tools will help your team be more proactive, reduce customer churn and provide you with more options to make more robust decisions. Partner with the first of its kind fully managed customer onboarding provider, like Instnt. We deliver the predictive analytics tools your organization needs — not a one-size-fits-all product. Try a free demo today!