Ask most people what ‘artificial intelligence’ means and they’re likely to say it’s something from the science fiction worlds of Star Wars or Terminator movies. While that might have been true when those movies were first released, artificial intelligence is now rapidly gaining ground in the real world.
Thanks to advances in areas such as high-density parallel processing and an extraordinary surge in available datasets, 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.
Artificial Intelligence 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, the speed of AI adoption is now rising quickly and shows 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 business’ bottom lines, 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 Artificial Intelligence
There is 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 analyze large volumes of data more effectively than humans. As an example, some financial institutions have begun to use AI applications to mine millions of consumer datasets containing details of age, job, marital status, and credit history in order to establish the risk profiles 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: Artificial intelligence- and natural language processing-based 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. AI applications-based asset management is, in fact, touted to be the next big thing in financial services.
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 cognitive technologies such as machine learning and deep learning, are facilitating high-frequency trading by analyzing 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 ascend. As a result, firms in the financial services sector need to develop long-term strategies for how best to take advantage of technology’s emerging capabilities.
Examples of recent developments in cognitive technologies that have the 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-added services using natural language processing.
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 maximized. New disruptive technologies based on machine learning and deep learning has the potential to be the next game changer, provided businesses follow these basic fundamentals. Firstly, the quality of data that is analyzed and used by AI tools must be first-rate. Failure to ensure this could result in suboptimal outcomes like poor asset management that tarnish rather than enhance customer relationships. Secondly, 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 disruptive technologies like machine learning and AI tools in the finance sector have only just begun. As the technology continues to develop, its potential to add value for companies and their clients will skyrocket.