AI: What’s your problem?

Beyond the Hollywood hype of futuristic technologies deposing humanity, AI is delivering pragmatic solutions with solid investment returns for financial institutions. It is driving positive jaws with both revenue growth and cost reduction with benefits being attained across a variety of corporate needs from compliance to brand integrity and recruitment.

This coming of age couldn’t come at a better time for financial institutions facing into increased competition, regulatory oversight and persistently sluggish revenue growth. Together these factors are decreasing NIMs, decreasing the gap between ROE and the Cost of Equity and increasing the case for efficient innovation.

In the favour of the financial institutions, they have significant stores of customer data, the financial resources to invest in sound business cases and no shortage of willing partners. The size of this opportunity is not being missed by global private capital. Investment in AI ventures has been steadily accelerating at 59.2% CAGR between 2010-17, with over 250 private AI tech companies being acquired since 2012.

But why all this fuss over another technology fad? The noteworthy factor is where and how AI innovations are emerging. AI is not just ‘a faster horse’ with task automation. It is not purely a cost reduction exercise. It represents and requires a fundamentally new way of looking at existing problems and the solutions that are possible. This is evident in the diversity of previously persistent problems being addressed from compliance to customer experience, and from strategy to recruitment.

Eliminating red tape

In 2014, the fourth Deloitte ‘Building the Lucky Country’ report described the $250bn, largely self-imposed, costs of red tape on Australia businesses. Companies like Sydney-based Red Marker are striking at this opportunity with products like Artemis, which creates and distributes relevant and compliant content in digital channels. It detects risky content as it is being generated, helping advisers and licensees to manage compliance risks associated with promotion of financial products.

The opportunities available from streamlining compliance processes are significant. JP Morgan estimates it is saving 36,000 hours of lawyer and loan officer time through AI-enabled document reviews and reconciliations.

Personalising customer experiences

At the front end, it has not just been about automating credit analysis or delivery of roboadvice, but also improving the analysis of customers and their feedback. NAB expects to save up to AUD16 million by 2020 by using AI in customer interaction tools. In 2017 it released a Kasisto-based virtual assistant to answer business banking customer queries regarding credit card enquiries, while NAB’s subsidiary Ubank uses a virtual assistant to support home loan enquiries.

Deloitte has been assisting banking and asset management clients to utilise the Salesforce Einstein AI platform to connect and make sense of large and diverse product, industry and customer data sources. These clients now have simple, actionable dashboards on the mobile phones of senior executive and sales teams, automatically flagging trends, risks and opportunities from machine driven data insights.

DBS is using IBM’s Watson to review customer chat logs and enhance the quality of customer interactions. Australian AI vendors like OpenDNA and Pascal51 are providing tools to drive psychographic profiling of customers and producing campaign logic and analytical insights in support of well-targeted marketing automation.

Trading strategy and analysis

While greater volumes of customers engage with machine-enabled roboadvisors and virtual assistants at the front end, machines are also performing real-time trading risk management. At JP Morgan, machines have found their place in trading strategy and analysis, monitoring fund flows, market sentiment and market movements in trading automation programs that represent in equal parts cost reduction and competitive advantages to drive new revenue opportunities.

This application of AI within trading teams is not focused on replacement of the human experts, but rather a turbo-boosting of their capabilities and insights available to them. Australian AI vendors like Symberra are aiding organisational distribution and adoption of the AI workload, providing an interface the simplifies collaborative use of analytical platforms for domain, rather than technical, experts.

Talent acquisition

A natural implication of this surge of AI activity has been the resultant talent war for AI skills to get the best of Australia’s AI talent pool, which CEB’s latest research estimates is at best ~3.8k. Fortunately AI is also delivering solutions to this with predictive recruitment models. Companies like Entelo, and Predictivehire provide fast and cost-effective collection and analysis of piles of structured and unstructured data. AI solutions like these can help these recruiters in candidate sourcing, screening, matching, or predicting a likelihood of job switch by an employee.

Developing your AI program

The question is not whether AI technologies are ready. It is not about which processes to automate. The challenge, rather, is one of change management. This question is how to educate the workforce about the new ways of working in tandem with machine intelligence that are now possible and how we might think differently about solving long-standing problems.

Employees need assistance to rapidly learn the potential applications and benefits of AI, as well as support in understanding the intended transition process as they face the fear of many unknowns in a future working alongside intelligent machines.


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