Predictive analytics is the process of attempting to determine an outcome using statistics, historical data, and computer modeling. Banks and financial institutions find predictive analytics particularly useful in forecasting market movements and assessing risk.
While it exhibits some similarities to machine learning, predictive analytics differs in that it does not progress autonomously but rather relies on human input. However, machine learning and artificial intelligence (AI) can be useful in improving outcomes and techniques related to predictive analytics.
Risk analysis is a key field in which predictive analytics is indispensable for the financial sector. Banks and insurance companies often use models powered by predictive analytics to do credit scoring and determine the suitability of clients. The models help in the decision-making process by analyzing key data points and comparing similarities in fellow policyholders.
The analysis is similar to procedures carried out prior to the availability of predictive analytics but can assess a greater amount of data in a shorter period of time. This results in far more accurate and reliable outcomes. AI can help streamline these processes and cut down on the level of human involvement, further reducing the cost and time required.
While predictive analytics is particularly good at crunching numbers and assessing financial viability it lacks AI’s strengths when it comes to identifying human idiosyncrasies. Advanced AI systems can now track behavioral patterns and pinpoint irregularities in massive data sets. This is key in helping banks to build an all-inclusive, fault-tolerant system that is autonomous, reliable, and available 24/7.
Digital banking, in particular, relies heavily on autonomous systems. Modern banking has evolved and customers now expect 24/7 service, instant and online. These models can now help banks to process the majority of their client’s requirements instantly and accurately without any human involvement.
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 once again show their strengths in the field of fraud prevention. Banks have to pay out thousands of dollars every day to cover losses incurred due to stolen account details. In some cases, customers can be held liable if they have not practiced due diligence.
Combining pattern recognition abilities and data analysis, AI and predictive analytics can spot potential intrusions in real-time. This can help reduce instances of identity theft and account tampering before it even happens.
Information from the Federal Trade Commission reveals that 50% of common identity theft is credit card related. Predictive analytics uses AI to ‘learn’ the telltale signs of a fraudster and pre-empt their attacks.
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.
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