Research and analyst firm Juniper Research projects that “total insurtech premiums generated by AI systems will reach $20.6 billion in 2024… with the continuing development of AI’s ability to offer enhanced, or even automated, insurance processes.”
While Insurtech is just one sliver of the financial services industry, it is one of the heaviest data-driven markets. Other sub-sectors of the financial services industry that use AI, according to this Towards Data Science article, include:
- Creditors: relying on AI to reduce costs while improving speed and accuracy of potential buyer assessments
- Risk management: utilizing AI algorithms to uncover potential issues of risk sooner
- Fraud prevention: employing AI to help prevent credit card fraud
- Trading: deploying AI models to perform high-frequency trades resulting in trillions of dollars while eliminating the potential of human error
- Personal banking: enabling clients to perform self-help through AI powered chatbots while reducing call-centers’ workloads
As the financial services industry continues to tap into technologies that analyze data — such as customer information — it will reinforce the value of AI and impact investment decisions. For leading-edge companies in the financial services industry, using AI or machine learning (ML) in their operations can be a competitive advantage. Those that are furthest ahead of their competitors have already made AI a core part of their business.
AI Turns Cost Centers into Revenue Generators
Today, financial services firms are leveraging AI to automate a variety of tasks. Using technology to perform many of these manual activities can make business processes more efficient and help firms identify areas of risk within those processes. While the list of potential use cases for AI is almost endless, our experience with financial services companies has shown us that many firms leverage AI for use cases, including:
- Processing claims for tax purposes and reimbursements by using techniques like optical character recognition (OCR) to transcribe and localize documents.
- Achieving business objectives by recognizing named entities in relevant text to identify key signifiers, such as purchase intent.
- Enhancing customer service through chatbots and data categorization via Natural Language Processing (NLP).
- Mapping merchant relationships by categorizing transactions.
- Detecting fraud by identifying victims of fraud and assessing risk by analyzing news stories and various financial documents.
- Enhancing the customer experience and mitigating risk by leveraging practices, such as “listening” to social media sentiment to gather consumer insights, sentiment analysis, and financial sentiment.
- Preventing data drift through data collection, enrichment, and categorization.
Organizations that have not yet operationalized AI may still be benefiting from use cases such as automating customer service and receipt transcription, or building models to handle risk assessment and management. These companies may not be ready to operationalize, but they all recognize the value of using machine learning and artificial intelligence to streamline operations, assess risk, and drive revenue.
By improving facets of the business that are typically a time and resource sink into more efficient parts of their business with the help of AI and ML, leading firms can transform and even identify additional revenue streams in the process. However, financial services firms’ forays into AI will be difficult if they do not first create high-quality annotated training data. With the appropriate tools in place, financial services firms can develop an appropriate data set and algorithm.
Technology Partners Can Help Develop AI Strategies and Data Sets
In order to take advantage of the promises AI offers, organizational leaders must understand how to get an AI initiative off the ground. AI, in practice, requires investments in people, technology, and processes. One prominent process is the creation of a high-quality training data set.
To build an annotated training data set, organizations can work with a third-party vendor that offers a combination of technology, experienced teams, and managed services.
When looking for a provider, aim for a vendor that offers a range of annotator options, including options that allow organizations to supply its own internal crowd or add different languages, to suit its needs. In addition, organizations will likely want a platform with built-in quality features, such as test questions for annotators, redundancy, and the ability to target specific contributor types. Finally, vendors that offer dedicated customer success resources will make onboarding, job design, monitoring, and optimization that much easier.
With a strong training-data technology partner in place, businesses can better ensure data quality and accuracy with their models.
It’s also imperative to consider privacy safeguards to prevent sensitive customer information from being compromised. Decision makers should consider prioritizing the following security and privacy capabilities in their platform research:
- Specialized NDA channels where contributors access tasks through machines that are owned and operated by the channel in a controlled and monitored physical location.
- Private cloud deployment, which can be hosted on an internally-managed cloud environment or hosted and managed by the provider.
- On-premise deployment that can be air-gapped, if necessary.
- SAML-based single sign-on (SSO) which gives members access to a chosen annotation platform through a selected identity provider (IDP).
AI Streamlines Operations, Minimizes Risk, and Builds Competitive Edges
The financial services industry is awash with data, and that data volume will only continue to grow. With the right strategy and technology partners in place, firms can leverage AI to streamline operations and minimize risk. Organizations that delve into AI now can help ensure they are able to make AI a core part of their business and gain a greater competitive edge now and in the future.