AI in Financial Services: Challenges and How to Overcome Them

By super.AI

Artificial intelligence (AI) is disrupting critical business processes in virtually every industry, and finance consistently reports the highest levels of AI maturity out of them all. By leveraging new technology like AI, the financial services sector has made banking applications and products more user-friendly and kept legacy institutions technologically relevant. However, this head start doesn’t mean there aren’t issues.

This article covers the challenges facing artificial intelligence in the financial services sector, including an overview of AI adoption, common obstacles and how to overcome them, as well as useful resources for taking advantage of artificial intelligence.

Adoption of AI in financial services

AI has been gaining prominence in the financial world since the 1980s, when expert systems were first used to predict market trends, provide customized plans, and reduce the risk of human mistakes. People working in the finance industry have been quick to recognize the potential of AI, and being early adopters has paid off. For example, hedge funds that leverage AI vastly outperform those that don’t. Research from consulting firm Cerulli found that “AI-led hedge funds produced cumulative returns of 34% over the past three years compared with a 12% gain for the global hedge fund industry over the same period.”

According to a 2020 World Economic Forum survey, 85% of financial institution executives are already using artificial intelligence, and 77% expect AI to become essential to their business in 2022. Separate research from O’Reilly Media found that people in the financial services sector have the highest levels of AI maturity when compared with other industries. Although AI adoption in financial services is far along in a relative sense, it still has a long way to go. Next, we’ll cover some of the obstacles facing AI adoption in financial services.

Challenges facing AI adoption

Early adoption means the financial services industry already has a well-defined list of industry-specific AI roadblocks. Data privacy and security, data silos, access to high-quality training data, satisfying regulatory requirements, and skills gaps are some of the most essential challenges for organizations to anticipate and prepare for in order to ensure forward momentum isn’t stalled due to unforeseen blockers.

Data privacy and security

Financial services providers must collect, process, and store huge amounts of sensitive data that requires robust security and protection protocols. Additionally, increasingly strict data privacy laws make it prudent for financial services organizations to be aware of existing and forthcoming regulations. When building AI solutions, financial services providers need to take the following into account:

• Fast and secure infrastructure: AI applications rely on massive volumes of data that must be securely stored according to industry standards, and highly accessible to ensure processing speed doesn’t render a solution ineffective. Ensuring a strong digital backbone and appropriate infrastructure that includes a highly secure, low latency connection to move data from where it is processed and back again is essential to succeeding with AI.
• Understand and follow relevant data privacy laws: The data that financial services providers rely on to build AI applications is typically personal. This is because it includes data generated from user activity such as shopping for new clothes, planning a vacation, or making investment decisions. New regulations such as GDPR and Payment Services Directive (PSD2) outline specific policies for handling this type of data, so it is important for financial services companies to be transparent with customer data and capable of explaining how they store and handle it.

Data silos

Siloed pools of data are a massive blocker for artificial intelligence. Either due to regulations, company culture, or technology, businesses often find themselves with siloed units that can’t (or don’t want) to be brought together. Unfortunately, AI doesn’t like this.

As mentioned above, AI relies on massive datasets that must be readily available for processing and analysis. Additionally, because financial services organizations collect and generate huge amounts of data each day, customer data is often spread across a number of different systems with varying degrees of compatibility. This complexity, combined with an unclear or altogether missing data governance program, presents a major challenge to AI.

Fortunately, there are solutions:

Tech-driven solutions

• Presto is an open-source, distributed SQL query engine built for big data. Its architecture enables users to query a variety of data sources such as Hadoop, AWS, MongoDB, and more. Data from multiple sources can be searched using a single query, making it possible to query data where it lives and unlock organization-wide analytics.
• By aggregating data into a cloud-based warehouse or data lake it is possible for companies to organize all their data in an accessible format. However, due to the type and size of the data stored by financial services companies, this may be a time and labor-intensive undertaking.

Process-driven solutions

In order for financial services providers to build effective AI solutions, a clear strategy must be developed to ensure data is accessible to everyone that needs it. Process-driven solutions to data silos may involve technology, but are focused on democratizing access to data across organizations. This includes:

• Defining a data management strategy: Drafting plans and policies designed to safeguard the integrity and accessibility of data across an entire organization is no small undertaking. At least one person needs to take ownership of data management, ensuring that both silos don’t impede progress and sensitive information isn’t shared unnecessarily or unintentionally.
• Formalizing access controls: As we’ve established, financial services providers collect and store highly sensitive information. The pursuit of more accessible data should be weighed against the potential risks of improved accessibility.
• Encouraging data literacy: Although only some (perhaps small) portion of a company’s employees will be directly contributing to AI application development, it is critical that everyone understands how to read, write, and communicate data in context.

Access to high-quality training data

It can be difficult for companies to source high-quality data, especially unstructured data. Of course, large technology companies like Apple, Amazon, Facebook, and Google collect huge amounts of data every second of every day. But smaller companies aren’t so fortunate. A former commissioner at the Federal Trade Commission (FTC), Rohit Chopra, went so far as to say, “Vast troves of consumer data collected by big technology companies allow them to gain a competitive edge and pose a threat to competition by creating entry barriers.”

Some pundits have argued for a “progressive data sharing mandate” that would require organizations of a certain size to share anonymized data with smaller rivals. Beyond this, structuring unstructured data varies in challenges.

• Open-source datasets: There are thousands of publicly available datasets that can be used for AI/ML projects. Additionally, larger financial services providers may have data they can use for AI application development, but remain unable to access it due to siloed data.
• Low-code and no-code AI: Thanks to rising computer power and declining computer costs, artificial intelligence has never been more accessible.

Overcoming regulatory obstacles

The financial services industry is heavily regulated, and unexplainable AI software poses a unique hurdle for regulators as they want (and need) to understand how a given model works. Explainable artificial intelligence (XAI) attempts to use methods and processes to ensure human users can trust and understand the results from machine learning algorithms.

Skills gaps

There is also another class of problems that center around a lack of resources and skills to implement AI at scale. It’s often easy to create a small demo project that looks really nice, but to implement at scale is something else entirely. Fortunately, no-code and low-code AI make it possible for non-technical business users to participate in building artificial intelligence solutions.

Are the benefits of AI in financial services worth the hassle?

Despite all the challenges mentioned in this article, artificial intelligence is very much worth pursuing for financial services providers. From cost savings to productivity improvements, the benefits are huge. A few of the upsides to adopting AI in financial services include:

• Enhanced transaction data: Identify hidden patterns buried in unstructured billing descriptor data, transforming unrecognizable merchant names into clear transaction records.
• Faster Know Your Customer (KYC) compliance: Automate KYC document processing to speed up new customer approval and onboarding.
• Intelligent document processing (IDP): Process, analyze, and extract relevant information from documents automatically.
• Improved customer experience and engagement: Generate personalized financial product recommendations customized based on user preferences and behavior.
• Operational cost savings: Enormous operational savings for financial services organizations that adopt artificial intelligence.

Additional AI for financial services resources

At super.AI, our mission is to automate boring work so that people can be more human. We strive to make artificial intelligence available to everyone with both the technology we build and the resources we create.