The Covid-19 crisis has provided an alarming insight into our frailties and points of weakness. As individuals, as businesses, as a society.
From disrupted supply chains, to an overwhelmed social care system, to online cyber threats, and growing numbers in the population reliant on benefits – we are far more vulnerable than we knew.
But in the same timeframe, Covid-19 has also highlighted to us all, how quickly we have been able to deploy new technologies, and digital capabilities to deliver solutions to current challenges. It is worth noting that only a year ago only 1% of GP consultations took place online – this is now standing at over 65% and it seems unlikely that patients (and GPs) will want to return to the old system. We have seen pharma companies deploying AI to scan through hundreds of terabytes of data about other known viral diseases and drugs to model Covid and predict drugs. Some AI companies are also trying to design new molecules, in case no currently available drugs deem effective. Again it seems unlikely that that particular genie will go back into the bottle.
And while there were concerns that some of AI’s business applications were leading to the loss of jobs and infringements on data privacy, these worries seem to have been set aside. Instead we’ve seen an acceleration of the use of robotics to do the jobs of people who have been ordered to stay at home or who have been redeployed. For example, robots are taking over floor and surface cleaning in hospitals, grocery stores and schools. AI is also fostering an increased reliance on chatbots for customer service at companies such as PayPal and on machine-driven content monitoring on platforms such as YouTube.
As we begin to scope out an environment after the immediate period of crisis – moving from “respond and redeploy” to “recover and reimagine” – all sectors have a real chance to ask how they can use the lessons learnt around deploying technology to better protect, serve and connect with clients.
Realistically a lot of these technologies have been around longer than we care to admit. Banks have played with a lot of these capabilities – but delivered remarkably little in concrete terms.
Within Financial Services this is encapsulated in the opportunity to move from a relatively limited use of AI to the core of the existing business and elevate it to transform the business more widely. By deploying AI and Machine Learning organisations can collect data from multiple sources and in multiple formats – extracting fresh insight. This in turn helps to deliver better understanding of client, market conditions and crucially new opportunities.
In the short term this will help simplify and personalize services. Natural language processing is already ubiquitous in most financial services organisations, but moving it from fielding calls to changing the ways services are designed, is where real value can be realised. AI meeting EI – using natural language processing to recognise when customers are stressed or anxious, allowing them to be offered additional support and/or escalation to emergency services. It also allows complex human verbal expressions and requests to be turned into machine relevant data – reducing response time but also informing product and service design. From basic changes like capturing the kind of information customers are regularly requesting and so informing website/app design to providing insight into the kind of product and services customers are looking for, and creating new merged, intelligent and adaptive bundles that are suited to what the individual needs and not what the company has to sell.
And while this has benefits to everyone, it has the most potential to help financial institutions to protect and serve their most vulnerable customers. It feels safe to say that the next 18 months to 2 years will be a business climate where we can expect more, not less disruption. As a result, vulnerability will remain at the top of agenda so being able to better predict and direct appropriate support helps.
AI systems reduce the need to communicate sensitive or distressing information more than once and can take that data to inform –technology creating services that are more aligned to personal needs. From as basic as learning software that recognises the stress in a customers tone in a routine 6pm call about paying a bill – and ensures that no calls are made to that young parent again at that time. To as important as using AI to spot payment anomalies and money laundering activity, allowing employees to focus on high probability fraud. Or as complex as AI monitoring social media and real time data feeds from government sources to predict and tailor financial assistance products, and identify in advance vulnerable groups and contact them in advance of any real difficulty.
AI isn’t a panacea. These are complex topics so AI can help but it will take time, costs will go up in the short term, and it requires access to data – which remains a sensitivity within most banks. It will also require organisation models which support cross functional teaming to enable or unleash the potential.
AI can transform the complex to the seemingly natural; the intangible into the concrete outcome; and put those often on the margins due to disability, health, age or income into a circle of care.
Over the coming months, financial institutions need to seize the opportunity provided by C-19 and start delivering a real sea change in the way they use these technologies to deliver on the promises of the last few years. At last.