04 Oct How banks are adopting AI solutions in the USA
We see significant investments happening in Banking Industry in the US in the last 10 years. This is especially true when banks are obliged to meet the intensified regulatory requirements which formed after many financial catastrophes. Competitive banks which focus extremely on Growth Agenda are increasingly adopting AI (Artificial Intelligence) as a strategic technology. AI can transform banking industry to face competitive threats, enhance customer experience and distinguish operational efficiencies.
This article discusses on the evolution of Artificial Intelligence in banking, a look back on the history of AI which has spurred this evolution and innovation to develop useful AI solutions which help and drive the banking industry. Also, it discusses on how fast Banks in the US are adopting AI to boost customer experience and drive revenues with special emphasis on Chatbots and Fraud Detection Products.
Banking is a data intensive industry. It is beyond the capability of a human brain to analyse and interpret the huge complex data patterns in each and every transaction which occurs in banking industry on minute basis. Artificial Intelligence can easily identify these data patterns and make relevant connections much faster. AI also improves customer personalisation and that is why Banking industry continues to adopt this technology. AI can answer numerous questions about routine banking issues in real-time.
Evolution of Artificial Intelligence in Banking
Artificial Intelligence as a concept was first devised in 1955 by Stanford Professor John McCarthy, he defined AI as the Science and Engineering of making Intelligent Machines. Simply put, AI is a specialised branch of Computer Science in which a machine simulates the cognitive functions that relate to the human mind. For example, banking revolves around numerous problem-solving scenarios which require in depth analysis and rapid development of solutions to numerous challenges. Accenture rightly puts it across as banks would increasingly interact with the customers through various AI platforms in the coming years.
The concept of AI as a disruptive technology is still in growing phase. Alan Turing believed that if humans use available information and reasoning science to solve problems and make decisions, why machines cannot implement the same tactic? Problems with computers unable to store commands was a major drawback which hindered growth of AI till 1974. Funding was another main issue. Historical demonstrations like Allen Newell and Herbert Simon’s General Problem Solver and Joseph Weizenbaum’s ELIZA, which was funded by Research and Development Corporation (RAND) further showed promising improvements in areas such as problem solving and spoken language interpretations in machines.
John Hopfield and David Rumelhart widely propagated “deep learning” techniques which permitted computers to learn using experience. Edward Feigenbaum instituted expert systems which mocked decision making practices of a human expert. Much of the landmark goals were accomplished by year 2000 and AI thrived as an emerging technology despite lack of government funds and public consideration.
How Leading Banks are implementing AI in US?
There is this popular and steadfast Contract Intelligence (COiN) platform which was introduced by J.P Morgan Chase which drastically saved human working hours by reviewing numerous documents at an unbelievable pace. This platform was designed to evaluate legal documents and extract relevant points/clauses. A normal review of around 12000 credit agreements which would take up around 360000 human working hours was finished by using the AI platform in a matter of few seconds.
Erica, the intelligent virtual assistant was introduced in 2016 by Bank of America. This chatbot uses predictive analytics and cognitive messaging and helps customers by providing customized financial guidance to attain their financial goals. Erica is available even out of banking hours and performs day-to-day transactions.
AI is used by BNY Mellon to reduce costs and eradicate repetitive tasks. 220 bots were deployed by them over the past 2 years to process automated tasks. Many tasks were automated some of which are account closures, trade entry, fund transfers, responding to data requests from external auditors, formatting and correcting data mistakes.
Citi Ventures, Citibank’s investment and acquisitions division invested into Feedzai which is a data science firm that can detect and eliminate fraud in real time. It monitors potential banking related threats at great accuracy and speed by conducting extensive analysis and perceiving any dubious customer actions.
Capital One came up with an interesting money managing chatbot which is a text-based platform called Eno. This was piloted for 100000 users which allowed them to manage money using mobile phones. Eno adapts itself according to user persona and learns customer behaviour overtime to serve them better. This chatbot accepts inputs in the form of voice commands just like Amazon Alexa.
Banks and other Financial Institutions are most likely to introduce AI/ML technology for Risk Assessment (49%), then for Financial Research/analysis (45%) followed by Investment/portfolio management (37%). Apart from this banks would introduce AI/ML technology in Trading (33%), Credit Approval Process (29%), KYC and Anti Money Laundering (29%), Regulation and Compliance (26%), Administration (17%) and Sales (17%).
Why Banks should adopt AI for Fraud Detection?
With the advent of newer platforms and interaction channels for customers to communicate and transact with the banks, chances and opportunities for complex fraud is also on the rise. Next-gen technologies like AI and Machine Learning (ML) lend a helping hand to banks in detecting typical fraud patterns. Banks have come to realise that sooner or later they need to tap into the total potential of AI including Machine Learning (ML), Natural Language Processing (NPL), Natural Language Understanding (NLU), Artificial Neural Networks (ANN), and Pattern Recognition to detect and prevent fraud instances.
In 2009, FBI cracked a huge phishing case which stole account details of numerous people to transfer about $1.5 million into fake accounts they controlled.
This is just one example to stress on the importance of adoption of Nuvento AI for fraud detection.
Let us quickly understand 2 main types of banking frauds and how AI can detect and prevent these:
This occurs when an unauthorized person seizes the login details of a customer and logs into the customer account. AI is widely used to detect and prevent such frauds by integrating biometrics into the login module in order to identify the customer using voice and face recognition technologies.
Whenever there is a deviation from the normal transaction pattern of customer, alerts can be triggered to detect the instance using AI and ML technologies.
Get in touch to know how our AI solutions identify, analyse and control fraudulent transactions and specious payments much before these are processed.
How Banks are leveraging on AI powered Chatbots to augment Customer Experience?
Banks are greatly focusing on providing differentiated experience to their customers who are always spoilt with choice. Advanced product recommendations and intelligent financial advice is what customers sought for from a trusted bank. Contextual decision making is a critical area and chatbots help with improving conversational banking by delivering insights proactively.
Chatbots engage greatly with customers by using advanced speech and natural language processing capabilities, also sentiment analytics to gauge the emotions, tone and voice accent to offer customised products/solutions based on the specific context in discussion. Nuvento AI Chatbots are effective in keeping costs under control and reducing resolution errors by minimising manual intervention which would enhance customer loyalty.
- Leading banks are leveraging innovative technologies to integrate chatbots into more progressive uses:
- Applying IoT technology in devices integrated with chatbot technology to converse with customers through voice.
- Facial recognition technology to facilitate zero-click transactions using chatbots.
- Visual illustration of the impact of long-term savings using virtual reality and chatbots.
- Providing real time status update on cross border blockchain transactions using chatbots.
All these technological advances enhance the process of collecting insights and apply advanced analytics to create unique customer benefits.