Digital technologies are continually driving disruption in business. Artificial intelligence (AI) is now at the frontline of disruption in the financial services industry. Many financial firms are looking differently at their processes, staffing, operation, and the way work is done in a human-machine partnership.

AI encompasses a variety of technologies, from autonomous or fully automated intelligence to augmented or assisted intelligence. Financial organizations are already adopting simpler AI technologies, such as the intelligent process automation that handles non-routine processes or tasks that require problem-solving to allow employees to work on more valuable jobs.

Financial services providers like banks have been using AI to redesign their anti-money laundering and fraud detection efforts for some time now, and investment firms are now using AI to manage portfolios, execute trades, and provide personalized services to clients. Insurance companies have been leveraging AI, especially machine learning, to enhance pricing, product offerings, and underwriting, along with strengthening the claims process, predicting and preventing fraud, and improving customer service.

AI is Exploding

Artificial intelligence is a field in computer science focused on building intelligent machines that can function like humans. AI is designed to perform human-like functions such as decision making, learning, speech recognition, and planning. The technology also allows machines to continually improve their performance on their own – without humans providing prescriptive instructions on how to do so.

AI consists of a multitude of technologies and capabilities, the most popular of which are:

• Machine learning: a method of data analysis that automates analytical model building.
• Deep learning: A branch of machine learning that’s been used to facilitate object recognition in images, activity recognition, and video labeling.
• Natural language processing: The ability of a computer program to understand human speech in real-time.
• Internet of things: Focuses on the idea that a wide variety of devices, such as vehicles, appliances, and buildings can be interconnected.

The effects of AI will be magnified in the coming decade, as almost every other industry endeavors to transform its core processes and business models to leverage the power of machine learning and AI. The bottleneck largely lies in implementation, management, and business imagination.

Near-term opportunities for cognitive systems lie in sectors such as securities and investment, banking, and insurance. These segments are rife with unstructured data, a great desire to harness actionable insights from this information, and overall openness to integrate innovative technologies.

In finance, Artificial Intelligence could help enhance operational efficiencies in different fields, from trading and risk management to claims and underwriting. While some applications are more relevant to specific fields within financial services, others can be integrated across the board.

AI in Financial Services: Risk Management

AI has proven to be extremely valuable in fraud detection and security in the financial services sector. With the advancement of learning algorithms, like those from deep learning, new features can be added to the system to help with the dynamic adjustment.

Fraud detection models can become more accurate and robust with cognitive analytics. If a cognitive system kicks out something it determines to be potentially fraudulent and a human determines it’s not fraud for various reasons, the computer learns from these human insights, and it won’t kick out a similar detection next time. This way, the computer is getting smarter.

AI in Financial Services: Trading

For years, investment management companies have been relying on computers to execute trades. About 10% of all hedge funds rely on large statistical models created by data scientists with PhDs in Mathematics (commonly referred to as “quants”). But these models typically use historical data, need human intervention, and don’t perform well with market changes.

The new technologies leverage techniques such as deep learning, evolutionary computation, and a form of machine learning known as Bayesian Networks. The resulting AI software can absorb huge volumes of data to make market predictions. To understand global trends, they can consume everything from tweets, books, earnings numbers, financial data, and international monetary policy.

AI in Financial Services: Robo-Advisors

Robo-advisors are computer programs designed to offer algorithm-driven, automated financial planning services with little to no human intervention. Robo-advisors help with monotonous or repetitive tasks like asset transfers and account opening. The process usually involved the customers responding to questionnaires about liquidity factors or risk appetite, which the robo-advisors interpret into actionable investment logic.

Most of the currently available robo-advisors allocate their client to managed ETF portfolios depending on their preferences. It’s expected that future capabilities will evolve into advanced offerings such as automated asset shifting and expanded coverage into alternative asset classes such as real estate.

Different players in the industry have adopted different approaches to robo-advisory. For instance, the smaller wealth management firms add algorithmic components to help reduce fees/costs, automate investment management, and compete with robo-advisors. On the other hand, established firms are buying existing robo-advisors or creating custom solutions.

Artificial Intelligence in Financial Services: Underwriting & Claims for Insurance

The insurance sector relies on balancing risk among pools of people – the insurers group similar people or entities together, as some people will require payouts while others won’t. the industry is built around risk assessment and risk management, and data analysis is not new to insurance companies. AI can help expand the amount of data analyzed and how it can be utilized, which could help result in more accurate pricing and other operational efficiencies.

Currently, insurance underwriters are leveraging actuarial models and computer software to evaluate the risk and exposures of potential clients, how much they should pay for coverage, and how much coverage they should get. In the future, AI will enhance the modeling, ideally highlighting the key considerations for human decision-makers that could have otherwise gone unnoticed. AI will also enable personalized underwriting by an individual or company, accounting for unique circumstances and behaviors.

Artificial Intelligence in Financial Services: Customer Service via Chatbots

Financial services providers are also making big bets with their customer-facing virtual assistants called chatbots. While the early versions of chatbots are only capable of responding to basic questions such as recent transactions and spending limits, future versions are bound to become full-service virtual assistants that can help track budgets and make payments for consumers.

Engaging with clients can result in significant cost savings, though human interactions are undoubtedly more complex than number crunching, which is rather straightforward. Critics often point to chatbots as lacking in empathy and understanding, which are fundamental when dealing with financial situations and decisions. Advancements in natural language processing technology will be essential for responding to personalized customer wishes and concerns.

Currently, many banks have apps that provide personalized financial advice and help with attaining financial goals. These systems can keep track of regular expenses, incomes, and spending behaviors, and then offer financial plans and suggestions. Most of these apps can also give out reminders to complete transactions, pay bills, and interact with the bank more conveniently.

Final Thoughts

On its own, AI has great potential, and its power grows exponentially when combined with technologies like blockchain, analytics, and the internet of things. And as businesses in different sectors embrace AI, it will continue providing unique opportunities, challenges, and risks.

Systems powered by artificial intelligence can be made more efficient, faster, and more reliable. Such systems are increasingly finding more applications in finance, and those that accept the risks that come with adoption are frequently rewarded by more productive and streamlined operations.

Still, before financial institutions can reap the full benefits of AI, they have to first overcome the glaring challenges, including privacy, security, bias, and regulatory issues. One of the main challenges may be the need to win customer trust.