Security and compliance mean that the financial industry tends to be slower to adopt new technologies compared to other, less regulated industries. However, artificial intelligence is starting to take hold in a big way.
While AI has been around for a long time, it is now becoming an everyday part of how we bank, invest, and get insured. According to Forbes, more than $4 billion in newly funded ventures focused on financial services AI applications in the last two years. Adoption is set to increase as more sophisticated technologies come to market with over 30% of financial services companies already leveraging AI in some capacity. In March, Accenture released a report that stated that 76% of banking executives intend to deploy AI within the next three years.
Financial institutions have leaped into AI by exploring various areas of their business where artificial intelligence can be applied with their focus on decreasing costs, enhancing revenue, reducing fraud, and improving customer experience.
Deloitte recently released a white paper titled AI and You: Perceptions of Artificial Intelligence from the EMEA Financial Services Industry. According to the paper, AI can be described in terms of three application domains: cognitive automation, cognitive engagement, and cognitive insight.
- Cognitive automation: Machine learning (ML), Robotics Process Automation (RPA), natural language processing (NLP) and other cognitive tools develop deep domain-specific expertise and then automate related tasks.
- Cognitive engagement: Systems that employ cognitive technology to engage with people, unlocking the power of unstructured data (industry reports / financial news) leveraging text/image/video understanding, offering a personalized engagement between banks and customers with personalized product offerings and unlocking new revenue streams.
- Cognitive insights: Cognitive Insights refer to the extraction of concepts and relationships from various data streams to generate personalized and relevant answers hidden within a mass of structured and unstructured data. Cognitive Insights allow detecting of real-time key patterns and relationships from a large amount of data across multiple sources to derive deep and actionable insights.
Have you noticed a change in the way you bank? Here are some of the ways financial services companies are leveraging artificial intelligence.
Online banking has made it more convenient for customers to check their balances, transfer money, pay bills, etc. but what about when you need help with something? Many financial services companies have started experimenting with chatbots. This replaces the need for humans to answer repetitive or straightforward questions like helping someone reset their password.
These text-based chat bots are just the beginning. Some banks are experimenting with upgrades to this automated service by leveraging predictive analytics and cognitive technologies to provide customers with highly personalized support. The virtual assistants will one day be able to access all of your financial information to help you make the best financial decisions based on your current situation and future goals without ever stepping foot inside a bank or talking to someone on the phone.
To make the best financial decisions, most times you need to analyze copious amounts of data. UBS is leveraging machine learning to scan large amounts of trading data to determine the best trading strategy in what they are calling ‘intelligent automation.’ While humans are still required to look over and approve the strategy, this technology upgrade increases the rate at which informed decisions can be made.
Human error can be very costly for financial institutions. To cut down on loan-servicing mistakes, JP Morgan launched its COIN (contract intelligence) program. This machine learning technology is used to review and interpret commercial loan agreements. This new technology is capable of cutting an estimated 360,000 hours of human work done by lawyers and financial loan officers.
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