Short and Long Term Perspective of AI and ML in Financial Services
Artificial Intelligence (AI) and Machine Learning (ML) are all over the news lately -- and for good reason. Almost every industry and endeavor can be touched by the existent and emergent tools that AI and ML make possible.
And, as Finextra reports, the UK banking world is in step with the latest developments:
The latest research from The Bank of England and Financial Conduct Authority, for example, suggests that 72% of UK firms in the sector are developing or deploying ML, a branch of AI that gives machines the ability to "learn" from data to improve computer performance.
The same report predicts that the median number of ML applications used by organisations in the sector will grow by 3.5 times in the next three years.
What Will Financial Services AI and ML Look Like?
Financial services Industry Strategist Viren Patel shares three visions of AI and ML adoption in the industry:
The short-term future could mean AI and ML tools being widely used, but in isolated, specialised cases – to streamline a few processes and provide insights to a few teams – without these tools being used in core operational decision-making. This approach would broadly mean carrying on with developing specialist financial uses for AI and ML, such as fraud detection.
Alternatively, it could mean continuing a current trend in financial services AI and ML, where much use of the tools is solely customer-facing, leaving internal operations largely untouched by the insights and automation they bring. That would mean using the tools for things like faster and more efficient onboarding, and developing virtual assistants to deliver more personalised support to customers.
Or, thirdly, it could mean both of the above with a crucial addition: operational data – analysed by AI and ML – forming a digital backbone that runs throughout the business.
Regarding the third approach, Mr. Patel further specifies three ways financial services firms could use AI and ML:
- streamline business operations through automation
- optimise employee experience by freeing people from repetitive, low-value work
- improve productivity
AI and Employees: A Positive Alliance
Unlike some people pessimistic about AI's relationship with actual human employees -- or even elimination of same -- Mr. Patel foresees a beneficial alliance:
Technology and people, working together, unlock real potential – enabling businesses to strengthen competitive advantage, be more responsive to customers, deliver greater economic and social value, and generate more meaning and purpose for individuals in their work.
Part of the advantage, he says, will come from elimination of repetitive manual data entry, sorting, scanning, indexing, and archiving -- "tasks would be done faster, with less opportunity for error – and people would be freed to focus on work that adds value."
Employees would see other assets as well:
In HR, for example, data can improve the impact of upskilling. Personalised learning experiences offer a route to improvement that's right for each worker. Automated assessments can deliver real-time feedback. And the most effective training can be identified based on skills, career goals, and emerging industry trends.
Three Factors to Keep in the AI Formula
Mr. Patel points out three factors he considers vital to AI and ML implementation:
Your teams are augmented, not replaced, by AI and ML.
It is critical for executives and -- perhaps more importantly -- team members to understand that AI and ML are not here to replace humans, but boost their productivity with tools that automate repetitive, laborious tasks.
Most financial institutions are well on their way to deploying AI and ML -- from customer service chat bots to automating operations like check processing.
Most recently, we are seeing a boon in AI and ML technologies for fraud prevention -- particular check fraud, where attempts and losses continue to rise. Financial institutions are integrating new AI and ML technologies like image forensic AI, which analyzes the images of checks and provides a risk score for the item -- providing details or "explaining" the reasoning for flagging an item.
As financial institutions continue their journey toward effectively leveraging AI and ML, it's crucial to be mindful of these three factors for successful deployment.