In recent years, Artificial Intelligence (AI) and machine learning have received much press, but the potential is obscured by fear and uncertainty among the general public and financial services executives alike.

In spite of the financial industry's long history of embracing new technology to serve its customers better and improve efficiency, it has been slower than other sectors in implementing AI solutions such as retail and healthcare.

As part of this presentation, I will discuss several compelling use cases for AI and why banks and credit unions must embrace cognitive technologies to transform the way they engage and serve customers.

Why AI?

• Artificial intelligence refers to technology that performs functions normally performed by humans (specifically, by people acting intelligently). The ecosystem has a wide variety of categories.

• From a study in 2018, it was indicated that 32% of financial services firms were implementing AI technologies, including predictive analytics, recommendation engines, and voice recognition. Those that did not adopt AI faced challenges such as fear of failure and regulatory compliance. According to the research survey, only 12% had not used AI because they felt it was too new and untested and so were unsure about its security.

• Artificial Intelligence and machine learning will be applied creatively in the financial services sector as part of the next frontier of automation. A number of financial technology (FinTech) firms have introduced time-saving features such as optical character recognition (OCR) for reading tax returns and other financial documents, which eliminates the need for humans to enter data into the system manually. AI-based applications like this are simple and reduce errors associated with manual data entry as well as save workers hours of rework each week.

• From research a broad range of industries will use cognitive systems to drive worldwide revenues from $62.35 billion in 2020 to grow at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028. Banking is one of the top two industries that will lead this growth.

Current Challenges & Solutions

• Since financial institutions generate enormous amounts of data every day, it is likely that they lack a consistent data governance program. Large banks, especially those that have grown through mergers and acquisitions (which is most banks), often have their customer data spread over multiple incompatible back-office systems.

• The problem can be approached in a variety of ways. Teradata, for example, has developed methods of accessing data in legacy systems and allowing it to be analysed. The company claims that data scientists can now spend 90 percent less time collecting data and more time analysing it.

• In collaboration with Facebook, Teradata has developed an open-source project called Presto, a SQL query engine optimised for interactive analysis and petabyte scale. In contrast to previous methods, Presto allows firms to query data wherever it resides, whether it is Hive, Cassandra, relational databases or exclusive data stores.

• Presto can process data from multiple sources, making it possible for a organisation to conduct analytics across all its data sources.

Benefits of AI

Some financial services firms are using AI despite the challenges, as the technology offers a variety of benefits.

Enhancing customer engagement: The importance of high-quality, personalised communications has never been greater in the financial services industry, which is increasingly focused on improving customer experiences. AI can help automate and scale this process.

Online wealth management services like ‘robo advisors’ offer automated, algorithm-based portfolio management advice without involving a human counterpart. These firms collect information from online users and then create an appropriate portfolio using low-cost ETFS and passive index funds from companies like Vanguard. It is possible for algorithms to rebalance portfolios regularly in order to maintain investment guidelines at costs less than 100 basis points, while advisors typically charge 2 to 3 percent annually plus trading commissions.

Others benefits include:

• Achieving productivity gains with automation
• Accelerating Fraud Detection and Minimizing Risk
• Helping consumers spend more wisely
• The future of interactions

In the financial services industry, AI is still in its infancy, but it will increasingly become important for organisations to stay competitive and innovate.

Artificial intelligence can improve communications with customers and staff, analyse data from multiple disparate locations to find patterns or connections that humans couldn't find, and answer investment questions in real-time via natural language. It's not too late to begin learning about AI technologies and strategising for the future-better late than never.

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