Artificial Intelligence (AI) needs no special introduction. In the previous post, we briefly touched upon how AI and Machine Learning are being put to use in Financial Services. But that was just scratching the surface. AI is transforming the financial institutions in terms of cost reduction as well as time-saving, intuitive services, etc.
According to a report by Autonomous, “Over $1 trillion of today’s financial services cost structure is exposed to replacement by machine learning and AI. Financial institutions should expect a 22% cost reduction in operating expenses due to AI, with most of the savings coming from the front office.”
The inclusion of AI in the Financial industry goes beyond simple process automation. AI can transform the finance industry by offering new approaches to computing strengthened by probability-based models that work almost like human intuition, hence making it more relevant for the customers or clients. “The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total, per Autonomous Next research seen by Business Insider Intelligence.” As per the report, the image showcases the main uses of AI in banking.
In light of the above statistics, let’s take a look at 5 ways AI is transforming the Financial Institutions.
We discussed this in the previous post too, but what does risk assessment really stand out in the banking sector? One of the major challenges for the finance industry has always been the identification of credit-challenged consumers, a threat that has grown since the financial crisis. What was once a function of simple heuristics, wherein customer asset analysis was done through surveys, studying customer behavior, etc. can now be handled using algorithms that keep evolving as the customer database increases. Banks now no longer shy away from employing cognitive technologies for risk assessment and management.
According to Capgemini insights “these non-traditional lenders use technology-based algorithms and software integrations to assess credit profiles of customers and are also leveraging alternative data such as social media photos and check-ins, GPS data, e-commerce, and online purchases, mobile data, and bill payments.” AI in risk assessment can deliver faster and accurate results and give reliable credit scorings for credit decision-makers by utilizing all of the financial and non-financial data available. And that too while decreasing operational, regulatory and compliance costs.
Fraud Detection and Management
According to a report by McAfee, “cybercrime and financial fraud are currently costing the global economy $600 billion.” Fraud detection and management is a high priority and it even tops the list in the Mercator AI Survey list as shared on a report 70+ Processes Banks Have Already Improved Using AI by ResearchAndMarkets.com. AI application in fraud detection is more on the machine learning side, a model where it can be trained to detect fraud within more than one type of transaction or application and do both at the same time.
Teradata, an AI firm selling fraud detection solutions to banks, has helped Danske Bank modernize its fraud detection process and reduce its purported 1,200 false positives per day. According to the case study on the Teradata website, “Danske Bank could reduce their false positives by 60% and were expected to reach 80% as the machine learning model continued to learn. AI implementation increased the detection of real fraud by 50% and the bank could hence refocus its time and resources toward actual cases of fraud and identifying new fraud methods.”
Financial Advisory Services
Peter Tilton, Senior Vice President, Digital of The Royal Bank of Canada, says that “RBC is focusing its use of AI on “solving real customer problems – and the number one source of anxiety for Canadians is managing their finances.” Quoting from an article shared by PwC, “Firms will likely move toward more advanced “augmented intelligence,” with tools that help humans make decisions and learn from the interactions. Firms can also look to AI as a way to customize product design and develop predictive analytics to improve outcomes such as reduced accident rates.” The likes of deep knowledge and experience with predictive analytics, machine learning, and natural language understanding technologies, etc. are being put to use in the financial industry to deliver AI-based financial advisory services, that are more personalized financial management insights and advice, and also assist in predicting future cash flow.
Trading using algorithms is nothing new. But when AI comes into picture it transforms into something bigger and more risk proof. As Michael Harte, Barclays’ group head of innovation, said, “the clearest use case for AI in banking is in large algorithmic trading, which means using vast amounts of high velocity data to outsmart the competition and to provide better instruments and value to customers.” AI according to many is sweeping the stakes in high-frequency trading, which is a specific type of algorithmic trading mainly identified by its high order rates and ultra-fast trade execution.
And as evolution progresses, some algorithms are starting to learn how to trade on their own through various machine-learning methods too.
While personalization has been the core of banking automation and AI implementations, it doesn’t come as a surprise that AI now is employed for managing personal finances too. Financial institutes are now employing new apps in personal financial management (PFM) which can help consumers make smarter purchase decisions, manage their finances, and make cost savings while they are spending their money.
Here’s an adoption example shared by Deloitte: “UBS used the help of artificial intelligence when delivering personalized advice to the bank’s wealthy clients by modeling 85 million Singaporean individual’s behavioral patterns. Fine-tuned for financial services, the technology allows Sqreem (Sequential Quantum Reduction and Extraction Model) to build a profile of an individual showing potential match-ups with different types of wealth management products.”
AI is here, while IBM Watson, Google’s DeepMind may have been the pioneers, more startups and digital innovators are focused on creating AI-based solutions to aid financial institutions in providing immaculate and secure customer service. What needs to be kept in mind is that AI isn’t a single technology but is a culmination of many different technologies that interact with each other, each offering something unique to the financial institutions.
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