The combination of large quantities of data and high levels of regulatory requirements make the financial services industry a perfect sector for artificial intelligence. It will come as little surprise that mass adoption of AI in financial services is expected by 2022.
Last time out we explored how AI is supporting risk assessment functions. In this blog I use a wider lens to look at 5 areas that Natural language processing (NLP). This rapidly evolving field of AI is supporting financial services to make big changes. This blog explores 5 trends.
1. AI in financial services for decision making
What defines your success as a business in one word? Your decisions. Decisions are reflections of the standards and regulations that make your organisation successful. When you have a team of people, several cognitive biases come into place, resulting in subjective outcomes, inconsistencies and other unwanted problems. Using an NLP-based approach, you begin to regulate organisational decision making. What you can measure, you can begin to control, and AI is able to capture the consistency and quality of decision making across your organisation. This means you can begin to identify the sources of poor company results. Those results could be financial, operational or strategic. It makes little difference to AI which just needs to be fed data and trained on what to look for. Once in place, AI can trace the decision making chain back to the department, team, individual and even data point level should you wish.
2. AI in compliance management
The way your organisation makes judgements is also extremely valuable. It involves detailed thought processes, substantial compliance with standards, and a great deal of regulation. In financial services there is no room for error when it comes to compliance as the penalties can be huge. The only option financial organisations have had to date is to increase headcount, an expensive and unscalable solution to the increasing regulatory demands and explosion of data in play. A survey from finance professionals expects the cost of keeping up to speed with regulation to rise to 10% of their firm’s revenue by 2022.
AI now offers a scalable, affordable option to this challenge. Deployed across key business functions, AI is able to run compliance checks en masse. This means a firm is able to maintain and control compliance with limited human input, a key reason this is one of the fastest growing trends of AI in financial services.
3. AI in reputation analysis
Judging something as good or bad, positive or negative, doesn’t necessarily have to be used only internally with your organisation. Sentiment analysis can be used to gauge customer perceptions about your product or service in the marketplace by analysing social media posts, product reviews and any other written content available online. Sentiment analysis has gained a lot of popularity especially on social media. This means your organisation can gain insights into how people perceive your company, its products and services, and ultimately score how likely they are to become customers.
With millennials and Gen Zers more sensitive to the way in which businesses act ethically and sustainably, the ability to measure your company’s reputation in numerical data is key in maintaining competitiveness. NLP achieves this by using a combination of keyword matching and statistical methods to understand the sentiment of each comment or post. As AI continues to advance, sentiment analysis is evolving to be able to factor in more nuanced communication such as negation and sarcasm.
4. AI in recruitment
Every HR professional knows how time-consuming it is to manually sort and rate incoming CVs. This tedious process often involves the identification of particular skills and experience that are preferred by the organisation as a way to pick out promising candidates. This happens to be a process that NLP can do extremely well. If you know what you’re looking for, for example experience in using claims processing, extensive keyword analysis could save you a great deal of time. Automatic clustering and semantic analysis aids recruitment significantly, giving a recruitment manager the ability to cluster CVs by similarity. This means you can filter applicants by experiences, skills and styles, or drill down deeper to look for language proficiencies, qualifications or specific competencies. Either way, recruitment gets streamlined and easier.
5. AI in competitor tracking
Market and competitor research is another time-intensive yet crucial practice that organisations must regularly undertake to maintain competitive advantage. Being able to accurately assess your organisation’s position in the market is critically in building a successful strategy. Financial results are a lagging indicator of market placement, with competitors only posting at the end of the financial period.
Reviewing competitors, customer sentiment and marketing messaging isn’t the most difficult process but it is very time intensive. AI is able to automate the collation competitor information, eradicating the most time-intensive part of the market research cycle. AI can also run 24 hours a day, 7 days a week, making competitor analysis and tracking a live process. The advantage of this is that the moment a key competitor updates their website messaging, for example in an attempt to break in to a new demographic, AI can instantly surface this information. This reduces the ‘information lag,’ providing your organisation with the ability to react to competitor activities instantaneously.
To learn how Fluence Arbiter™ is aiding financial services organisations in application processing, and how Fluence Advisor™ is bringing deeper business intelligence to C-Suite teams, get in touch at firstname.lastname@example.org or call 0121 638 0760.