When financial institutions engage in rethinking their operations in this way, it can be a catalyst for AI transformation. Ideally, it will be founded on a culture and paradigm shift that creates new organisational structures and – within them – suitable roles, relationships and responsibilities. As Scotiabank’s Phil Thomas explained of his firm’s own AI transformation, it involved “getting the org structure right and creating a culture that’s accepting of the use of AI”.

An AI-fuelled digital strategy can foreground the concept of zero operations or “zero-ops”. This entails reducing human activity to a minimum, wherever the well-chosen automation of manual tasks can reduce costs and increase revenues. resulting sin an improved all-round service. AI applications can help achieve this zero-ops goal by replicating or exceeding human skills in a range of specific and closely defined tasks.

Modernising operations

In an IBM study published in January, financial services firms were asked how they planned to modernise their operations over the next five years. Of these respondents, 46 per cent aimed to modernise by increasing their use of data analytics and AI, and 41 per cent intended to deploy AI techniques to personalise services. At Confirmation,  in our own 2021 report Letting go of legacy IT Systems the number one most urgent improvement to the infrastructure was technology to mitigate risk in manual processes.

Chatbots, for example, are a widely used AI-based front end, using techniques like natural language processing to personalise customer service. They carry out activities such as dealing with queries or complaints, collating documents, or onboarding new clients.

AI has made a number of advances in onboarding, including the novel area of adverse media screening (AMS). AI software is used to scrutinise prospective customers against potentially negative data sources in social and other media. Deutsche Bank’s "successful implementation of intelligent automation” used AMS to “accelerate and strengthen compliance” during the onboarding process.

After developing applications such as chatbots and an AI regtech application – converting regulatory texts into compliance obligations – CommBank Australia has recognised that, by using AI, they can detect issues more quickly. They can also interpret patterns in transactional data, or recommend changes to the bank’s business or its products.

AI for financial compliance

The latest Thomson Reuters cost of compliance report asked what skills compliance officers would require in the future. The answers indicated how important advanced technologies are to the culture of financial compliance. Subject-matter expertise came out as the pre-eminent skill, covering a wide swathe of disciplines including “cyber, regtech and AI”.

Cyber applications figure prominently in the AI world, with “threat hunting” combining traditional methods and AI techniques to detect high-risk network activity. Firms are enhancing cybersecurity by comparing current and historic patterns in network traffic to alert staff to potential cyber threats.

Alongside these more recent innovations, AI practitioners are building on a decades-long history of developing AI-based credit scoring, loan, and fraud detection systems. The growing sophistication of machine learning techniques has enabled firms to automate these kinds of operations with increasing success.

The strategic challenge for financial organisations is to combine this broad and ever-expanding range of AI applications including existing ones and those in the pipeline. This is so that they cohere with each other and slot neatly into the firm’s IT infrastructure. For example, AI applications are dependent on extensive, well-structured databases – digital transformations that embrace a zero-ops approach have a remorseless appetite for vast amounts of data.

Data has been a central concern in developing our secure online platform, Confirmation. We are constantly seeking new ways to make a secure channel of accurate, authenticated data readily available.