Human Brain Power and Artificial Intelligence aren’t mutually exclusive AI enhances the decisions only people can make
“The organisations that strike this balance between person and machine will be the most successful in today’s increasingly digital landscape.” Chris Doner, founder and CEO of Access Softek. As one of the modern world’s rapidly advancing technologies, analysts predict global investment in AI is set to hit $204 billion by 2025.
Artificial Intelligence, Machine Learning (ML), Robot Process Automation (RPA). They’ve been buzz words for around ten years, but most businesses haven’t known how to apply the technology in real world contexts or at scale. What do the terms mean and how could they enrich the financial services industry? Let’s cut through the jargon and clarify the four main types.
- Voice and facial recognition
- Natural language processing (NLP): the automatic manipulation of natural language, like speech and text, by software
- Machine learning: a branch of AI which focuses on the use of data and algorithms to imitate the way humans learn
- Deep learning: a subset of machine learning. It automates much of the extraction activity, eliminating some human intervention and enabling use of larger data sets. AI researcher Lex Fridman calls it “scalable machine learning” in an MIT lecture
The mechanisation of simple, repetitive tasks usual in back-office and accounting processes is becoming commonplace but AI can now be used to automate more complex tasks that demand intelligent practices. Primarily employed in fraud analysis and threat reduction originally, companies are recognising the wider benefits of data value-extraction, particularly once they succeed in amalgamating information from disparate silos for analysis. Innovations in ‘data fabric’ support quick access to external data that’s protected by fire walls or spread over different locations. An end-to-end data integration and management solution, it makes data rapidly available and supports real-time customer service and analytics. It’s a powerful way to improve efficiency and accuracy while cutting costs. The Financial Brand writes, “Three out of four C-suite executives believe that if they don’t scale AI in the next five years they risk going out of business.”
Big data is big business. By 2025, we’ll create and consume 180 zettabytes of data globally, pushing the market’s value to $103 billion by 2027 (1 zettabyte = I trillion gigabytes). The vast amounts of information available will reveal, in the right hands, valuable insights into customer behaviours, preferences, personalities and beliefs so that hyper-relevant content, products and bespoke pricing can be formulated. Talent isn’t abundant, however, and employees with the right skills to optimise AI technology are needed for successful transformation. Almost a third of banks say lack of skills have been a barrier.
Financial service companies are keen to exploit ever smaller pieces of information to differentiate themselves and their services. Data and AI are fundamental in their drive towards enhanced customer experience and product personalisation. As start-ups and the number of HNWIs proliferate, greater wealth management expertise and targeted products are needed. CNBC reports that more than 8% of American adults are millionaires. A human-centric model has been the norm for private banking and recommendations and advice are likely to have been influenced by personal bias, experience and limited information. Annie Brown in Forbes remarks, “AI can take in large sets of data and identify the best choice among a large set of products.”
Algorithms aren’t replacements for investment experts though. Rather, the rich experience and skill professionals have amassed over years are merged with AI to augment the information and advice clients receive. The emergence of no-code development platforms is fundamental: they’ve made AI accessible to non-technical and technical users. Using NLP in investment research and analysis enables extraction of the most important insights, generates summaries and produces suggested next steps in investment decisions. Early alerts to credit issues, supply chain complications and ESG concerns are possible.
The FCA announced early in November it’s to use AI in its data management to improve insights and cross-team collaboration. The FCA’s datasets are currently spread over different internal and external systems. An Insight Engine will interconnect these datasets so employees can conduct rich context searches across data formats. The Engine also creates visualisations of search results for data and non-data specialists to encourage cooperation between departments. The Engine’s risk alerts will subsequently feed into data training models for employees. Even, or perhaps especially, with the FCA, however, strong AI governance is crucial. Increased use of the technology raises questions about the transparency of decision making and the accuracy of algorithms.
Ultimately people, not algorithms, are better at making complex decisions, forming relationships and inspiring loyalty. As Chris Doner notes, “Technology can help strengthen these relationships but customers will not be forming relationships with technology alone.” Despite this, with 92% of wealth managers believing Amazon, Facebook, Apple and Google will expand into their areas of operation, the industry will need to embrace without hiding behind every technological tool available to remain competitive.