As we become increasingly dependent on digital operations and adapt to online platforms in the wake of a changing landscape, Artificial Intelligence (AI) is changing the way businesses operate and invest. With continued pressure to remain innovative, the financial services sector is moving to cloud-based platforms and AI-enabled solutions to provide the best solutions for customers.
At its core, AI enhances our ability to leverage the large volumes of data generated in day-to-day business activities. It enables us to identify patterns, make predictions, create rules, automate processes and communicate more efficiently. For financial service providers, AI capabilities are all very relevant due to the data-intensive and technology-dependent nature of the industry. In fact, we are seeing a lot of growth and opportunities across customer, risk and operations functions - some examples of which we'll cover next. It’s no surprise then, that AI is at the top of the agenda for financial services.
Customers are becoming more informed and expect transparent as well as consistent and reliable services; often these services are accessible on a 24/7 basis. AI can be leveraged to obtain a holistic view of the customer and provide timely support.
The Global regulatory Outlook 2020 has indicated that 33% of banks have allocated or will allocate more than 5% of their annual budgets to compliance. This is a major opportunity to leverage AI technologies, however according to Deloitte’s State of AI in the Enterprise, 3rd edition, only 4% of the respondents in financial services used AI in legal and compliance.
A large Australasian bank used AI for outlier detection to validate a model calculating investment projections. This enabled the identification of areas for potential improvement that traditional model validation techniques would struggle to pinpoint.
AI can help automate and standardise process flows and create financial sustainability by reducing operating costs. Furthermore, automating repetitive tasks helps employees focus on high-value activities.
A large Australasian bank uses speech analytics on check-in calls related to loan repayment deferrals. The calls are passed through AI models trained to detect vulnerability, flagging interactions that needed specific manual review and helping identify the customers that most needed additional support.
The examples above have all used validated AI tools to recognise patterns, create rules or improve communications channels. However, the use of AI for prediction and forecasting poses additional risk, especially when these predictions are based on personal data. This is because models are built from a chosen subset of available data and are not only prone to bias but assume that patterns from the past will continue into the future.
For example, credit scoring models have famously had issues with predictability. An example is the models incorrectly accepting or rejecting mortgage approvals. This occurs not only because they are prone to bias but also because low socioeconomic and minority groups tend to have less available data in their credit history.
It’s fair to say that AI is successfully being used in several areas of the financial services industry but there are some areas where AI will not be as impactful, or be more risky to implement. Banking and financial services organisations should therefore be smart about choosing appropriate use cases and technologies to generate value.
A good use case should outline what ‘measurable’ success looks like both in the short and long-term. Use cases that are small and focused allow for a ‘fail-fast’ scenario, giving the team time and flexibility to improve and iterate.
Once use cases have been generated, they need to be assessed and prioritised based on the level of business impact and technological feasibility. These use cases can then be categorised as one of the following:
A common misconception is that AI use cases require bleeding-edge technologies to generate value. The financial services sector has many opportunities to use proven AI technologies that have been in use for several years. For example, classification type problems are commonly seen in banking. Classification algorithms learn from a categorised training dataset to then classify new data into these categories. The following transformative case study is an example of using AI to classify high-risk calls for further review.
AI-driven call centre compliance automation
A leading Australian bank wanted to ensure it was meeting its compliance obligations in relation to secured lending phone calls with its customers. The bank faced a challenge with checking compliance across thousands of calls with their customers and needed to confirm these conversations were consistent, appropriate and compliant with regulatory requirements around the customer onboarding process. Compliance had many facets, such as providing required disclosures, customer authentication checks as well as questions around income and expenses.
The bank was also seeking to understand their customers’ sentiments, including vulnerability and confusion, and ensure their customers were receiving the necessary support.
By applying AI techniques to automate compliance on the bank’s secured lending calls, they were able to achieve high impact outcomes. This included:
A further consideration was the responsible and ethical implementation of the technology and guarding against unfair bias. By analysing the quantitative fairness of key models and training data as relates to protected features (e.g. gender of call centre agent), it was ensured that no key fairness risks were being left unchecked in the implementation. The initial application of AI techniques to this area of the bank was considered a Transformer use case, but given footwork performed in a High Impact area of the bank, roll-outs of the technology to other areas would be considered No Brainer.
With so many opportunities to apply AI in the banking sector, it’s important to prioritise potential applications by the business value they would generate. It is also important to consider the risks, to ensure that customer experience isn’t disrupted by unforeseen events.
Implementation of AI technology requires a full roll-out cycle and experts at every stage, there needs to be clear communication on how the new technology will be used and why. It is useful to break the high-level plan for full roll-out down into smaller phases that deliver benefits early - starting with lower complexity and building from there.
Finally, be clear on how the new technology will be used and why you are doing the work in the first place. By having the customer and service delivery at the core, it will help create a foundation for implementation of new technology and help build a use case for why it should be implemented.
Isidora Labra Odde
I lead the Data Science & AI practice of Deloitte New Zealand's Risk Advisory team, using a variety of machine learning / AI technologies to detect and manage fraud and operational risks. Examples of the work I and my team do include: Automated document analysis (e.g. employing natural language processing in contract analytics), Model risk management (e.g. model validation, bias detection) Outlier detection (e.g. using unsupervised machine learning to identify fraudulent transactions), Predictive modelling (e.g. using supervised machine learning to model employee churn) I am an experienced data scientist and project lead, with a 12 year track record delivering operations research and artificial intelligence solutions cross-industry (Financial Services, Public Sector, Energy, Retail, CPG, TMT). I am a registered Project Management Professional, and have published postgraduate research on the application of machine learning in the project management domain. Before joining Deloitte in 2014, I gained six years' experience managing business improvement projects in the public sector. My current focus is on the Financial Services industry.
I lead our Analytics & Cognitive offering in Auckland and am passionate about helping clients become ‘insight driven’ by harnessing the power of analytics, automation and artificial intelligence (AI). Many businesses are making a fundamental shift from experience-based judgment to data-driven decision making that enables business outcomes. This requires organisations to extend their focus beyond technology and data to encompass the essential elements of strategy, people, culture and process. At the same time, they need more certainty that their investments in these essential building blocks will actually deliver business value. I enjoy helping clients on the journey to maximise the value of analytics and information that deliver real business value.