Artificial intelligence is becoming more and more present within the financial services industry. Whether it is helping banks determine an individuals credit risk, or helping consumers finding the services that are right for them, AI offers many benefits to finance.
Michael Ouliel is the founder and CEO of enterprise AI company BlackSwan Technologies and has been an active leader in the technology sector for more than 20 years. BlackSwan Technologies is based in London, England with offices in the United States, Israel, Poland and Germany.
Here Michael shares his views on how AI is transforming personalisation in financial services.
It is estimated that by 2023, the aggregate potential cost savings for banks from AI applications will reach $447 billion. Most banks (80%) are aware of the potential benefits presented by AI, but many are impeded by the difficult process of managing data. While there is no question that AI will continue to rapidly transform the financial sector in the coming years, key questions remain. Namely, how are global banking leaders overcoming persistent barriers to AI implementation and where do AI techniques drive the most significant results?
The key to AI’s power in finance lies in the technology’s ability to synthesise massive amounts of data and achieve fresh insights to help banks make well-informed decisions. AI tools that don’t require highly organised data sets can enable much more efficient and effective data exploitation by bank employees, who simply do not have the bandwidth to investigate millions of individual data points and identify patterns and relationships among them.
This inability to capitalise on existing data to personalise customer interactions is a key challenge. A Global Financial Services Consumer Study conducted by Accenture found that one in two banking consumers indicated an interest in personalised financial interactions. Consumers are so enthusiastic about personalisation because other brand leaders (think Netflix, Amazon, Spotify) have used AI to generate highly individualised recommendations which make the user experience less time-consuming and more satisfying.
At BlackSwan Technologies, we have seen financial institutions use AI in similar ways to gain a deeper understanding of consumer behaviours in order to provide more personalised services. A few of the ways that our customers use AI are detailed below: gaining background on clients to enable more personal conversations, identifying potential new customers, and targeting new product promotions.
Fostering individualised conversations between banking managers and clients is key to great customer service, but deepening client knowledge is extremely labour-intensive for banks without the support of artificial intelligence. AI-enabled social listening applications can harness available information from public domain conversations and transactions and combine this with unstructured internal data like email and phone transcriptions. Insights from this data can be represented in the form of a ‘knowledge graph.’
Knowledge graphs contextualise millions of data points and present key takeaways, which allow bankers to remain constantly alert to their clients’ interests and financially significant events such as home sales and legal proceedings. For wealth managers in particular, this is a critical function that helps promote deeper connections and drive investment activity. One of BlackSwan Technologies’ wealth management clients, applying social listening, dramatically improved customer satisfaction survey scores, reduced investor churn, and surpassed revenue growth forecasts.
Lead generation is a similarly labour-intensive effort for banks, which often assign hundreds of analysts and marketers to the search for potential new customers. These employees are faced with the same insurmountable challenge of dealing with data efficiently and effectively. Financial institutions can use AI to outline the “perfect” customer profile by mapping their customers’ and employees’ relationships and characteristics, and taking into account multiple factors such as customer profitability, cost of relationship maintenance and potential growth.
Machine learning can then be used to compare the ideal profile to external data sources in order to detect potential new customers, and alerts can notify sales teams of prospective customers based on the given criteria. AI can also increase outreach efficiency and effectiveness by indicating the best tactics for approaching different customers. As a result of these AI techniques, one of BlackSwan Technologies’ banking clients discovered previously unknown qualified leads and harnessed the best tactics to approach each potential new customer. The bank significantly surpassed its corporate customer acquisition targets, improved new customer average profitability and reduced acquisition costs.
Targeted Product Marketing
Credit card companies often struggle with accurate, large-scale personalisation of marketed products for their millions of customers. In the highly saturated and competitive credit card market, impersonal marketing can lead to costly customer turnover rates and stale margins. Credit card companies can use AI to monetise their existing data assets by unifying customer interactions and purchase behaviour to generate straightforward suggestions for analysts. For instance, AI can identify customers that use competitor-issued cards, allowing for targeted discounts to draw dual card-holders away from the competition. A leading credit card issuer used BlackSwan Technologies’ behaviour-based personalisation capabilities to drive additional sales, improve the customer journey, and increase retention rates.
In sum, in order to remain competitive, financial institutions must shift their legacy business and operating models to adopt what McKinsey calls an “AI first” business model. Adopting AI as the foundation of a reimagined “business operating system for the enterprise” will enable significant revenue growth through increased efficiencies, new value propositions and distinctive customer experiences.