The Rise of AI Tools: Verticals to Watch, Part 2

In part 1 of this series, we shared some ways industries such as healthcare and automotive are beginning to use artificial intelligence (AI). We also looked at the Internet of Things (IoT) and how AI will continue to improve the user experience of products such as wearables and smart devices (for example, Google Home).

It may be easy to understand the potential for artificial intelligence to improve how doctors diagnose diseases, and we’ve all seen plenty of news about smart cars and smart homes. But even traditional industries, such as finance and insurance, are starting to explore how they might benefit from AI. In this post, we’ll review some of the benefits AI has to offer these industries. We’ll also take a look at how AI is being used to protect us online and in the real world.

 

Finance

If you consider that AI is only as good as its data, then data-rich industries such as FinTech (financial technology) stand to benefit from the coming wave of AI-driven solutions. Both internal (think compliance and regulatory use cases) and customer-facing applications will roll out in the coming years. Companies such as Clinc are working with financial institutions to deploy an AI platform that works like “Siri for your bank account.”

AI can also be used to qualify customers for financial services based on their past behavior. If a potential customer has a habit of overdrafting or missing payments, AI can use that information to predict whether that person should be eligible for certain services. AI can also help the industry support customers by prescribing the best time to communicate with these clients to promote the right behavior.

Customers will benefit as well. AI can find attempts of identity theft by scanning documents (or people) to verify that a person is who he or she claims to be. In fact, Gartner is predicting that by 2022, AI-driven security tools that combine machine learning, biometrics, and user behavior will be the norm, and passwords will account for fewer than 10 percent of all digital authentications.

 

Insurance

A promising use case for AI in insurance companies is the automation of tedious claims-processing tasks such as analyzing a claim for fraud or claim adjudication, which is the process of comparing the claim against the policy coverage. This will free up employees to work on decidedly human tasks, such as customer engagement. While the algorithms process the data related to an incident such as damage to items or inconsistencies in a client’s story, humans can focus on communicating with clients, thus leading to a better customer experience and less churn. (AI becomes especially relevant when calculating the lifetime value of your customers.)

Another way insurers are leveraging AI is with wearable devices. Insurance companies are using the data collected by smartphones and wearable devices to encourage preventative healthcare in their clients. Using data, insurance companies can incentivize customers to live lifestyles that will lead to better health outcomes for them and a better risk/reward ratio for the insurer.

 

Security: Cyber and Otherwise

AI is powering the next generation of cybersecurity solutions. Current use cases include everything from insider threat detection to antivirus software. In the case of the latter, AI can monitor internal communication for red flags. Even more exciting, AI will be able to cut off threats like Wannacry by rapidly learning how a virus works and then countering it.

Historically, security incident and event management (SIEM) technology is programmed using static rules to detect cybercriminals. Using data generated by past attacks and white hat hackers, these solutions aren’t able to execute beyond what they’ve been told to look for. By augmenting these existing systems with AI, cybersecurity pros are able to program SIEMs and security information management systems (SIMS) to watch for new kinds of attacks.

IBM’s QRadar Advisor is leveraging natural language processing to be able to deliver a first attempt at analyzing a security threat. According to Rohan Ramesh of IBM’s Watson for Cybersecurity team, “The system might tell a security analyst, ‘I believe this event is related to ransomware, and here’s the underlying evidence I’ve used to reach this conclusion.’ ”

Automating drudgery is something we all can look forward to. You can also look forward to the next post in our series, which will take a look at how the field of education, the media, and e-commerce are using AI.

 

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