AI Applications: an Overview of 8 Emerging Artificial Intelligence Use Cases

It’s difficult to expand the already existing growth of opportunities in banking. The market is saturated with the growing pool of fintech companies planning to provide banking products on different, sometimes better terms than banks do.

Banks carry a heavier regulatory burden, which changes constantly and in some cases stifles innovation⁶. Legacy systems that banks employ are also dragging down their growth, while fintechs have the opportunity to start with an already competitive tech stack. Nevertheless, many of the banking leaders already apply AI applications to achieve incredible productivity and efficiency gains.

Artificial intelligence is becoming a quite lucrative risk management tool for banks, especially given the abundance of robust financial datasets. The opportunities are endless as practically any banking line of business has use cases, which can be dramatically improved through advanced predictions, generated by AI.

The examples of these innovations are abundant. JPMorgan uses COIN, a machine learning system that analyzes commercial credit data. The system saves the company hundreds of hours of manual labor¹⁴ and minimizes credit processing errors.

Customer churn prediction is a popular AI use case among financial institutions, especially given the rising competition from other banks and fintech startups. The beauty of AI apps in this niche lies in the fact that they offer intimate insights about the specific churn reasons. This data can be used to drive online service improvements and personalization¹⁵.

This is one of the biggest niches in healthcare AI, poised to become a $2 billion industry in the next couple of years¹⁷. Although medical imaging is not a new application of AI, we decided to place it into the emerging technologies list because of the incredible growth that it’s been experiencing over the past couple of years. Since 2014 over $1.2 billion dollars¹⁸ have been invested in artificial intelligence in this domain.

However, that’s just a footprint of the whole market. The leaders in this domain are already building effective AI systems for image recognition.

• UC San Diego developed an AI system that can diagnose eye diseases with a 95% accuracy, just like an experienced ophthalmologist¹⁹.
• Samsung’s AI technology that detects breast lesions in ultrasound images is said to have increased the diagnostic accuracy by 5%²⁰.
• Nvidia made the SDK for its Clara Compute Platform available to the general public. It was already used by a tandem of hospitals in the Boston area to develop an algorithm that recognizes abdominal aortic aneurysms²¹.
• The leading educational institutions create separate research units to explore AI in medical imaging, like the Stanford’s Center for Artificial Intelligence in Medicine & Imaging²².

These and other developments will push AI in medical imaging to the next level. But for now, there are still many obstacles and issues that AI software developers in this domain have to overcome. For example, the interpretability of models that make predictions is an issue since most of these models are based on deep learning algorithms, which are almost impossible to explain.

The insurance market for AI is growing at a very rapid pace, as legacy approaches to insurance predictions are being disrupted by various technological advances. The industry has a history of working with data²⁹, which is the foundation of any modern AI system. Insurance is also famous for hoarding data about its customers to improve various operations, like underwriting, claims management, and reserving. That’s why AI is on track to revolutionize the insurance market with use cases popping up in all parts of the insurance lifecycle.

According to a survey by SCOR, these are the operational areas that will experience the biggest transformation due to artificial intelligence³⁰.

Apart from these generalized areas of interest, there are specific insurance use cases that can be greatly augmented by artificial intelligence and machine learning systems.

We’ll focus on the areas that could potentially experience the highest impact from artificial intelligence: claims management and underwriting (based on the Insurance Nexus study, cited above).

Underwriting is a manual process that includes a variety of repetitive tasks, which, by default, means it has the potential to be automated with artificial intelligence. The modern insurance customer is not going to wait for application approval but instead will go to a different insurance company. So instead of wasting underwriting resources on rubber-stamping generic applications, AI should be used to identify policies that can be automatically accepted or, on the contrary, require an underwriter’s review. And many insurers are already betting on digital AI applications to disrupt their legacy approaches to policy management and pricing.

In healthcare, AI is expected to completely automate the underwriting processes for 60% of applications in the near future³⁵. The accuracy of these AI systems got to the point where insurers are issuing policies to traditionally risky and uninsured customer categories, like people with chronic diseases and non-citizens.

These potential productivity gains are why the underwriting AI market is already getting its "unicorns", like Planck Re, which received $12 million in its round A of funding³⁶.

However, there are companies offering much more exotic tools for underwriting. One of the examples is EagleView that uses artificial intelligence to catalog and identify property details, which can be used in daily life to refine the underwriting process and ultimately fine-tune pricing for property insurance. Such image recognition technology could also be used to assess damages after major insurance incidents, like hurricanes and floods.

If we take a look back at some of the use cases in this report, it becomes obvious that you need people to generate insights and operationalize them. All of the available data science specialists and AI engineers can’t possibly cover the demand for machine learning and AI.

That’s why Gartner predicts³⁷ that augmented analytics (AA) will be one of the biggest trends in the coming years. AA means that data scientists, engineers, and business analysts will be aided by automated systems that can build, deploy, and update machine learning models automatically. This will save thousands of person-hours and take productivity to the next level.

These capabilities have the potential to transform how healthcare, banking, and insurance companies are exploring AI and iterating on their findings. Some of the major products in this niche are offered by Dataiku, H2O Driverless AI, RapidMiner, DataRobot, and AWS SageMaker.

AI has become an integral part of modern business processes. While transforming everyday life, there are examples of artificial intelligence revolutionizing the way people address challenges in various areas.

The heavily regulated banking sector can use the power of artificial intelligence to increase the effectiveness of anti-money laundering investigations, thus being more accurate in identifying fraudulent activities and getting enough time to prevent them. AI also broadens the banks’ risk management capabilities by allowing them to predict credit risks, customer churn, collection, and investment risks with higher precision.

Owing to great volumes of data processed in healthcare, the industry is to benefit immensely from AI. From treatment and operational optimization to readmission risk management—the scope of healthcare domains that AI consultants can foster through intelligent technologies is impressive. Medical image analysis is one of the fastest developing technologies and it’s able to improve the diagnostics outcomes significantly. At the same time, healthcare providers can use disease analytics to excel in predicting diseases, thus getting the opportunity to foresee them or cut their progression.

When AI comes to the insurance area, it can foster such domains as claim management, underwriting, fraud prevention, customer services, sales, and marketing recommendations. The greatest thing about AI in insurance software is that it brings automation to various resource-heavy and time-consuming processes, which allows insurers to reduce human effort while improving the insurance outcomes substantially.

Apart from several industry-specific AI applications, there are at least two increasingly popular AI domains that will influence various spheres. While augmented analytics (AA) is being developed as a new paradigm of automated data preparation and sharing, robotic process automation (RPA) coupled with AI can become a new turning page in the history of autonomous systems that will likely accompany or even replace humans in a variety of business processes.