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Learning from Successful Machine Learning Use Cases

3 min read

Solutions in AI and Machine Learning (ML) are already established and being successfully applied across industries, especially in finance. In a UBS Evidence Lab report, 75% of respondents at banks with over $100 billion in assets say they are currently implementing AI strategies.

The concept of leveraging ML has been explored and adopted, the technology is available and leading industry players possess vast amounts of data, just brimming with ever-growing business value. What then, can other industries learn from successful applications of ML?

Preventing fraud

Fraud is a significant issue among banks and financial services companies, with losses amounting into billions of dollars each year. Since finance companies store vast amounts of data, it increases the risk of cyber attacks and / or security breaches. As technology advances, cyber attacks are also becoming more sophisticated, creating an even larger threat to the finance industry.

In the past, fraud detection solutions were designed based on fixed and static rules, which would eventually be bypassed by advanced cyber attackers. Thanks to machine learning, cybersecurity is now more adaptable and fluid when protecting sensitive data and preventing fraud.

Machine learning solutions can evaluate transactions by comparing them against other data points, such as the user’s usage history, location, typing behavior and so on. Through this evaluation, fraudulent transactions can be flagged and even rated in terms of severity, thus notifying the user or even terminating the transaction on the user’s behalf.

Since machine learning works by scanning large data sets to detect anomalous activities, then flag them up for further investigation by the relevant team in charge, this is easily applicable for all industries that manage large amounts of sensitive data, including insurance, legal, healthcare, government, etc. Players in these industries can adopt machine learning to prevent fraud before it happens, fostering increased trust with their customers.

Enhanced customer satisfaction

Tech-savvy customers in today’s fast-paced world expect their questions answered, and they expect it fast. Chatbots are already deployed in financial services to automate the answering of commonly asked questions, service requests, etc. However, chatbots don’t always understand the users’ requests and can go off-topic, often adding frustration to an already strained situation.

AI and machine learning solutions can bring chatbots to a more adaptive level, by analyzing information gathered from past interactions and then “guiding” chatbots on how to better respond in future sessions.

Machine learning can even help chatbots identify frustration or other emotional cues within a user’s wording, then adapt subsequent responses to better handle the situation. This could include replying with more “understanding” words or immediately connecting the frustrated user to a live customer service personnel. Conversational AI solutions can even be programmed to understand local dialects, making the service more inclusive and familiar for a wider range of users.

To further enhance customer service, machine learning can identify certain situations and propose unique solutions for the customer. For instance, when a customer’s past transactions showed that payment was consistently made at the last possible date, this might indicate tight cash flow. Upon identifying this pattern, machine learning can offer the customer a different due date or even a personal loan to help the individual make punctual repayments.

In industries that deal with a high influx of commonly asked questions or frequently sought-after standard services, machine learning can help streamline the customer service and engagement. Sectors like healthcare, government agencies, ecommerce, IT and even FMCG can drastically reduce resources dedicated to answering FAQs and fulfilling standard requests.

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Automate intensive processes

Perhaps the most impressionable benefit of machine learning automation is JP Morgan’s usage of AI, to shorten a task that originally took 360,000 hours, to mere seconds. In this situation, lawyers and loan officers who were originally involved in the time-intensive, mundane task of interpreting loan agreements were suddenly freed up to focus on other key areas of the reputable bank.

Machine learning’s ability to analyze and process lengthy documents at speed can help banks and financial institutions to meet with compliance issues and even identify and prevent fraudulent activity. Other processes that machine learning can automate is the processing and prediction of problematic trades, money laundering and more.

Industries that involve labor-intensive tasks like document processing and data processing can benefit greatly from machine learning, which potentially shortens tasks and increases efficiency, allowing enterprises to better utilize their manpower for higher-value projects. Industries like legal, insurance, accounting, government agencies, healthcare, IT and ecommerce can leverage machine learning to not just process information, but also predict what outcomes might occur. From there, businesses can make more informed decisions and respond better to prevent potentially costly scenarios.

Enjoy the same benefits and more, by consulting an expert from Adnovum on how your company can leverage machines learning to potentially save your business at least thousands of man-hours, so as to invest them in more high-value areas.

Register for a complimentary consultation on Machine Learning

Published July 28, 2022