Thanks to advances in areas such as high-density parallel processing and an extraordinary surge in available data sets, adoption of AI and other cognitive technologies is expanding at an ever-increasing rate. If it’s not already being used inside your business, it soon will be.
AI and finance
As is the case in many sectors, the financial services sector has begun using AI and related disruptive technologies. While initial deployment tended to be slow, speed of adoption is now rising quickly and showing no sign of abating.
When you think about it, the business case for financial institutions to invest in AI is quite compelling. With low interest rates continuing to have a detrimental impact on the business bottom line, banks are constantly on the hunt for ways to boost operational efficiency and reduce costs.
Another big driver of AI adoption across the financial services sector is the increasing need to comply with strict regulations across different jurisdictions. Since the introduction of regulations such as the European Union’s Payments Services Directive (PSD2) and the United Kingdom’s Open Banking program, banks want to be able to harness rich customer data for building better apps and services without falling foul of the regulators.
Applications for AI
There are a range of emerging applications of AI and machine learning in the finance sector which are attracting growing attention. Some examples include:
- Credit and insurance underwriting: Firms active in these areas are using AI and machine learning tools to process customer applications faster without diluting risk assessment standards. The key advantage is the accuracy, scale, and speed of the AI tools, which can analyse large volumes of data more effectively than humans. As an example, some financial institutions have begun to use AI to mine millions of consumer data sets containing details of age, job, marital status and credit history in order to establish the risk profile of individual applicants.
- Fraud Detection: Unlike more conventional financial fraud discovery systems that were based primarily on a predefined checklist of risk factors and a complex set of rules, AI-based fraud detection can proactively spot anomalies and flag them for the attention of security teams. The tools are also likely to reduce false positives which benefits both the firm and its customers.
- Workflow Automation: Growing numbers of financial institutions are now using natural language processing (NLP) to automate certain business processes in an effort to reduce expenditure and increase customer satisfaction. One example of this is the rising use of customer service chatbots that can answer questions without the need for the intervention of a human operator.
- Asset Management: AI tools are also being put to work within a new breed of financial firms dubbed ‘robo-advisors’. These operators are offering algorithm-based, automated financial planning solutions to clients that can help them to build investment portfolios that are aligned with their individual goals and risk tolerance. Again, this can be done with little or no human intervention.
- Algorithmic Trading: Meanwhile, increasing numbers of hedge funds are using complex AI-based systems to make thousands or even millions of trades each day. These systems, based on machine learning and deep learning, are facilitating high-frequency trading by analysing a wide range of market factors in real time. They can do this much more efficiently than humans.
A strategy for the future
The rate of development in the AI and machine learning space is continuing to climb. As a result, firms in the financial services sector need to develop long-term strategies for how best to take advantage of the technology’s emerging capabilities.
Examples of recent developments that have potential for the sector include the growing array of voice-activated digital ‘helpers’ such as Google Assistant and Amazon’s Alexa. These could offer new channels for firms to interact with their clients and provide value-adding services.
When embracing opportunities such as these as part of an overall strategy, it’s important for financial firms to also keep a couple of factors top of mind to ensure that return on investment is maximised.
First, the quality of data that is analysed and used by AI tools must be first rate. Failure to ensure this could result in sub-optimal outcomes that tarnish rather than enhance customer relationships. Second, it’s vital to be aware of the need for robust data security to ensure personal details are not compromised.
The deployment and use of AI and machine learning tools in the finance sector has only just begun. As the technology continues to develop, it’s potential to add value for companies and their clients will skyrocket.
Sudip Lahiri, Senior Vice President and Head Financial Services - Europe at HCL Technologies
