Source – thefinancialbrand.com
Computer scientist and celebrated futurist Ray Kurzweil says artificial intelligence will match human intelligence by 2029, and that by 2045, it will have multiplied the human biological machine intelligence of our civilization a billion times.
Predictions like this abound amid the hype surrounding AI. Real understanding however, is less common. Many enterprises are unclear about what constitutes AI, where it can be applied, and how to prioritize its use cases within the organization. In a survey commissioned by Infosys on the state of AI adoption, half of the 1,600 respondents said that not knowing where AI could assist was one of their biggest challenges.
A good understanding of the technologies under the umbrella of artificial intelligence is key. Many of these technology pieces have seen rapid and impressive evolution in the recent past, so much so that it does not allow banking providers the luxury of waiting until it matures. Here are the most important building blocks of AI and their use cases in the context of financial services.
1. Machine Learning
Machine learning refers to the ability of software to learn on its own without being programmed. Machine learning programs adjust their algorithms in response to new insights. Where data mining algorithms would hand over findings to human beings for further work, machine learning can act on its own.
Banks and credit unions can use machine learning across the front, middle and back office, in functions ranging from customer service to sales and marketing to fraud detection to securities settlement. For instance, in the middle office, they can identify or even prevent fraud by deploying machine learning to look into patterns in payment transaction data to spot anomalies or inconsistencies.
2. Deep Learning
Deep learning leverages a hierarchy of artificial neural networks, similar to those in the human brain, to do its job. Unlike traditional programs, which think linearly, deep learning mimics the human brain to perform non-linear deductions. Deep learning systems produce better decisions by factoring learning from previous transactions or interactions to draw conclusions. For example, they can gather information about customers and their behaviors from social networks and from that infer their likes and preferences. Financial institutions can use this insight to make contextual, relevant offers to those customers in real-time.
3. Natural Language Processing
Natural Language Processing (NLP) is a key building block that will help computers learn, analyze, and understand human language. NLP can be put to use to organize and structure knowledge in order to answer queries, translate content from one language to another, recognize individual people by their speech, mine text, and perform sentiment analysis. Besides improving customer service, natural language processing-capable systems will, over time, learn to resolve issues automatically.
Banking providers have started to leverage NLP in different ways. On the website of the largest bank in the United States, virtual assistants offer support for credit cards, loans and other banking services. Singapore’s DBS Bank uses a virtual assistant called KAI to enhance the experience at Digibank, its mobile-only bank in India. KAI helps Digibank to anticipate and answer thousands of customer queries, and assist customers with their banking transactions in real time.
4. Natural Language Generation
Natural Language Generation (NLG) is also a foundational AI technology. Wherein NLP will help computers analyze, understand, and make sense of human language, NLG will help them to converse and interact intelligently with humans. Banks mainly leverage NLG for purposes that require data from multiple sources to be combined to produce insights in a format that is easily understood. NLG can also knit raw data into a narrative, which banks such as Credit Suisse are using to generate portfolio reviews.
5. Visual Recognition
Visual recognition is a branch of AI that recognizes images and their content. It uses deep learning to perform its role of finding faces, tagging images, identifying the components of visuals, and picking out similar images from a large set. Visual recognition feeds off huge amounts of data and needs open source software libraries and frameworks to function well. A key application of visual recognition technology in banking will be similar to that of speech recognition — enabling a frictionless customer experience.
To this end, several banks have adopted visual recognition in common front-end operations. Australia’s Westpac, for example, is using the technology to allow customers to activate a new card from their smartphone camera, while Santander is one of those using it to authenticate documents. And many other banks, including Bank of America, Citibank, Wells Fargo, and TD Bank, are leveraging this technology to allow customers to deposit checks remotely via mobile.