Artificial Intelligence and Machine Learning: Is it Human vs. Machine?
The terms ‘Artificial Intelligence’ and ‘Machine Learning’ seem to be buzzwords in the finance industry.
Artificial Intelligence (AI) and Machine Learning technologies are set to revolutionise an industry based on numbers but also an industry still traditionally dependent on human expertise, analysis and creative intelligence to progress and prosper. Some proponents of these processes believe that it will be a symbiotic relationship between man and machine. Others believe that their introduction will mean the demise of the human worker. So what are these concepts and how will they impact the financial services?
AI vs Machine Learning
Artificial Intelligence is a board term, but was succinctly defined by Andrew Moore, Dean of the School of Computer Science at Carnegie Mellon University as “the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence”. A modern day example of AI would be Apple’s beloved digital personal assistant Siri, who can assist in finding information, creating events and providing directions purely based on voice recognition. Another example would be self-parking cars, whereby the vehicle will park itself using spatial and proximity information without any human intervention.
Machine learning is a branch of AI that relies on analysing data to automatically improve itself through experience. Netflix has fully incorporated machine learning into its platform by using predictive technology to make recommendations based on what the viewer has previously watched or rated. Music streaming providers such as Spotify or Pandora also use machine learning to recommend new artists based on what music users have listened to in the past. Recommendations continue to get refined and improved as the platforms continue to learn and analyse the users’ choices.
Implications to Financial Services
Based on how society has already embraced these powerful and useful technologies in other sectors, it was only a matter of time before they infiltrated the finance industry. A study conducted in the UK identified that 86% of business leaders in the financial services sector said they were already using these technologies. The World Economic Forum published a report earlier this year indicating that AI is fundamentally changing the physics of financial services. The bonds that have traditionally held together the constituent parts of financial institutions have been transformed ushering in a new age where data equals capital. Manual processing is giving way to programmed automation. Generic campaigns are being replaced with targeted marketing. Algorithms are usurping spreadsheets. The fabric of payments is evolving.
Advanced Data Processing & Automation
According to McKinsey estimates, banks do not realise the value of more than 80% of the total data collected by them. Therefore, there is a data mine that is waiting to be tapped. AI will help organisations realise the full potential of its data. AI can effortlessly consume large amounts of data, process the information faster than human efforts and can provide insightful outputs based on inference. The more data that can be processed, the more refined and accurate the data analysis results.
By allowing AI to extrapolate from data, companies will gain insights on their customers, which can lead to more customised products, services, communication and advice. The speed of the computation can be leveraged to enable a faster feedback loop, which will continually learn and provide updated insights thus allowing adaptability of product development and marketing strategies. Organisations will also see increases in productivity as a result of automation and machine learning. Time-consuming work such as compliance reporting, customer on-boarding communications and administrative documentation can be made more efficient and accurate with AI-powered automation.
Personalised Customer Experience
These technologies will give rise to a more personalised customer experience. One example is the use of chatbots. Chatbots are automated chat systems that are designed to simulate human interaction. Chatbots identify emotion and context within text and will respond in the most appropriate manner based on previous interactions. Bank of America recently implemented its own chatbot or resident digital financial assistant named ‘Erica’, which has been widely recognised as a successful initiative. In a press release earlier this year, Bank of America confirmed that Erica assisted with 8 million client requests. Personalised communications will allow organisations to ‘humanise’ what can be quite structured and cold processes and give the consumer the façade of having a human on the other end.
Although AI can lend itself well to customisation, it can potentially lead to other unwanted behaviour at times. Predatory lending or marketing, where individuals are targeted based on information gathered through machine learning, are only some examples of how organisations or individuals can exploit these technologies. As such, industry policies and standards relating to privacy and prudential behaviour must be continually reviewed and updated as the industry continues to adopt AI and machine learning in various degrees. Financial ethics will play a big part in how AI or machine learning will continue to be accepted in the financial services industry.
The introduction of artificial intelligence or machine learning does not equate to a bleak future for the human professionals. Computers can be tasked with doing the repetitive and tedious jobs such as data processing. Instead of having to manually troll through a copious amount of historical data, a financial advisor can provide customised advice with a click of button. Employees will subsequently have more capacity to undertake higher level responsibilities and expand their skillsets. AI will alleviate some of the monotony of certain jobs and create new focus areas for professional development. Certain types of individuals will embrace this change while others may not. Organisations will need to look at investing time and money into transforming their talent alongside their technology to accommodate this fundamental change in an employee’s role.
Better Fraud Detection
Machine learning has been fundamental in enhancing fraud detection in the financial services industry. Indue’s Financial Crimes service is a prime example of how talent and technology co-exist to provide a whole that is more efficient than the sum of its parts. The service has embraced the benefits of machine learning with its foundation in the Safer Payments platform, which leverages machine learning algorithms to continually enhance its fraud detection capabilities. The platform is a neural engine that analyses a large transactional data pool to detect certain patterns and flag any anomalous behaviours. Indue’s financial crimes specialists leverage the cognitive computing provided by the platform, but strengthens the process by performing the executive decisioning and customer engagement that is critical to fraud management. The platform assists with pattern detection, data modelling and predictive capabilities whilst the specialists provide the emotional intelligence that only humans can offer. This reciprocity approach has been fundamental to the success of Indue’s Financial Crimes service.
To find out more, visit Orion Financial Crimes