AI in Finance: 3 Hurdles to Overcome
Feb 27, 2020
- AI in finance is growing and touches every level of financial service organizations.
- Organizations in the financial services industry are not always prepared from a personnel, strategy, or skills standpoint to make the most of AI.
- AI has become a major factor in driving digital innovation in financial services and is critical for organizations that want to stay ahead of the curve.
The financial services industry is investing heavily in artificial intelligence (AI), with many organizations having either adopted or beginning to adopt this technology in some capacity. But while banks and other financial organizations have started to experiment with AI, they sometimes struggle with getting these initiatives off the ground. Below, we highlight some of the big hurdles that financial service organizations face when adopting AI so that these companies can better prepare to address them.
Using AI for Financial Services
Artificial intelligence impacts every department within an organization, from marketing to HR to finance.
On the front end, AI can be used for customer service purposes. Chatbots and voice assistants can be used to interact with customers, as well as create more personalized email marketing experiences.
On the back end, AI can be used to detect fraud, speed up the underwriting process, automate reporting, and more.
Ultimately, AI allows financial institutions to deliver better customer experiences, find new opportunities to drive revenue and reduce operating costs. For example, according to Autonomous Next, AI is expected to save banks $447 million by 2023.
One way this is achieved is with the use of robotic process automation (RPA) which, according to Gartner, costs a third of the price of an off-shore employee. Another example is Capital One’s Eno, a chatbot that uses natural language processing (NLP) technology to act as a digital banking assistant for customers.
AI in Finance – Three Hurdles to Overcome
1. Data Management
Machine learning is one of the most common types of AI that is commercially used and is perhaps the most well-known. Machine learning requires a lot of data, and that data needs to be correct to generate accurate results. For a company in the financial service space to reap the benefits of machine learning, it will need to have cleaned up, consolidated, and standardized its data. This can be difficult for large, global organizations to achieve, especially if they are using outdated legacy systems. This is why consolidating to a single cloud-based ERP makes sense for organizations looking to use machine learning; when all data is housed in a single solution, data can be more easily leveraged by a machine learning application.
2. The Human Element
Since AI is a relatively new technology and highly technical, the average employee isn’t going to fully understand its capabilities, limits, and potential use cases. While many AI-powered tools are designed to be simple to use and don’t require that the employee have a data science background, the tools still require practical knowledge to maximize the value. This means that employees using AI tools will need at least some level of training.
Fortunately, the barrier for entry is going down, especially as AI-driven features are added to existing solutions. For example, if a bank needs to fill an open senior investment analyst position, it’s now possible for human capital management software to use machine learning to identify which skill set is needed to thrive in that position and identify others in the organization that could potentially fill that role. Features like this already exist in modern tools and are relatively straightforward to use.
There are some scenarios in which it is necessary to recruit additional personnel for AI-specific roles, such as hiring a machine learning engineer to design custom programs. Filling these roles can difficult, as they are in high demand.
3. Knowing Where to Start
Because the applications of AI are many and, as mentioned previously, can be applied to any department of a financial service organization, it can be challenging to determine where the point of entry should be.
Organizations need to first consider what their goals are before investing in AI. In other words, leadership should ask, “what are we looking to accomplish, what challenges do we need to overcome, and how can AI help?” Once these goals are in place, leadership needs to come up with a strategy for gradually implementing AI initiatives throughout the organization, starting small and gradually expanding as the organization builds on its successes.
Cultivating Digital Innovation in Financial Services
Artificial intelligence is helping to drive digital innovation in financial services. Eventually, it will be hard for financial services organizations to avoid AI. According to Gartner, AI will be incorporated into approximately 80% of emerging technologies by 2021. Few technologies are as transformative as AI, and as the technology matures, we will see new, innovative ways to use AI. To keep up with these advances, financial service organizations need a solid technology foundation that can easily incorporate these innovations as well as have on-hand expertise to adapt these innovations to best meet the business’ goals.
Popular Articles About Financial Transformation
3 Ways CFOs are Leading Digital Transformation
Why Healthcare CFOs Don't Trust Their Data (and How Digital Innovation Can Help)