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AI application in Financial Services: Ready for revolution

Posted by Dnyanesh Patkar
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For close to half a century Hollywood has been preparing us for Artificial Intelligence (AI). Stanley Kuberic's 1968 classic, 2001: A Space Odyssey became famous for its standoff between HAL 9000 and an astronauti. Today, HAL 9000 would be recognized as a sophisticated cognitive bot using natural language processing. More recently we saw Duncan Jones' Moon where an astronaut shares a lunar habitat with Gerty, an AI bot, as his only companionii. But we don't need to look into outer space for AI any longer. AI is everywhere. It is driving our cars, flying drones, evaluating mining sites and guiding industrial processes. And then there is Swedbank's Nina, the AI-powered chatbot, who conducts about 30,000 customer conversations per month. Nina made headlines with a first-contact resolution of 78% in its first three months of operationiii. Nina symbolizes the radical AI-led transformation taking place in the financial services industry.

Nina isn't alone. Barclays has a voice recognition services for its corporate customers; Santander recently became the first UK bank to provide secure transactions to 15 million customers using voice recognitioniv; Royal Bank of Scotland is providing 1,200 of its staffers with AI that helps respond to customer queries fasterv. B1NK in Kazakhstan recently launched a chatbot via Telegram that speaks three languages and even answers questions about how bots spend their free time.vi Bank of America wants to introduce Erica who will help customers keep track of budgets, save money, manage debt, recommend investments, track credit scores and complete transactions sometime later in 2017vii. And MasterCard is all set to unveil Kaiviii. Going by evidence, it is practically an AI arms war that has been launched in the industry.

In the world of finance, AI is not about technological razzle-dazzle. It is about making one-on-one services, which are normally available to elite banking customers, expand at scale. It is about being personal, intuitive, contextual, intelligent, highly available and reliable.

For an industry known to embrace technology with open arms, chatbots are just the beginning. They shine a light on the potential of AI technology to serve customers with greater care and attention, help create new products, reduce fraud, bring down costs and drive growth.

A global study done by The Economist Intelligence Unit (EIU) and sponsored by Wipro in the second half of 2016 called Artificial intelligence in the real world: The business case takes shape put some interesting numbers behind the AI trend. According to 75% of the respondents in the studyix, AI will be "actively implemented" in their companies within the next three years. Ben Goertzel, chief scientist at Aidyia, an AI-powered hedge fund based in Hong Kong told EIU researchers: "We're seeing a burst of energy in machine learning, deep learning and other kinds of AI. Every major financial firm is hiring loads of AI experts now."

What type of applications will finance leaders prioritize for AI-driven enhancement? And where will AI have the largest impact over the next five years? The top three functions identified by respondents in the study were Customer Interaction (30% -- which explains why customer-facing chatbots are generating so much interest!), Risk & Compliance (28%) and Financial Analysis (26%).

The use cases for AI in financial services are varied. They go much beyond applications that offer personalized financial advisory and friendly assistance.

AI will be able to analyze larger data sets to make better insurance underwriting decisions for individual customers instead of segments based on geography and broad demographic profiles. These applications will blend structured and unstructured data points, use algorithms, business rules and statistical models to answer questions like "If a blizzard pummels Philadelphia what is the dollar loss likely to be and which high-value properties should I pro-actively target in order to minimize claims?"

Similarly, applications will use data, behavior patterns and personality traits to make better lending decisions. These applications will leverage data to predict and avert default. They will provide default management advice and appropriate incentives for customers that meet regulatory criteria, thereby minimizing risk.

There are a number of innovative ideas that AI will fuel, changing the financial services landscape. For example, AI applications will help set up and organize innovative social networks for individuals who want to pool their resources. These resources can be used to make loans to each other or be used as collective investment corpus.

The day isn't too far when AI tools will monitor user behavior and network activity to pick out signs of fraud, helping fight financial crime. This is an especially interesting area where AI has a significant role. In the digital era, new patterns of fraud are emerge continuously make traditional approaches to fraud management ineffective. But AI, that uses Machine Learning to keep improving system capabilities, will identify and flag fraud in real time. This means faster response to changing fraud patterns.

A key area where AI can be rapidly deployed - and demonstrate quick wins and ROI-is the Know Your Customer (KYC) process. KYC is a document-intensive process with 80 percent of the data identification, aggregation, extraction, verification and capture done manually by analysts. Content required to complete KYC forms is hidden in scores of documents with no predictable structurex. AI can automate and simplify the KYC process using super-intelligent (machine vision) and super-fast process (industry specific models and rules). Our experience shows that cost savings can be as high as 30 to 40 percent using AI.

"Ultimately it (using AI) starts with improving the customer experience," said Chris Gelvin, chief operating officer for Group CEO Functions at UBS, a Swiss bank. "Customers will notice the faster responses, reduced error rates and new insights we're able to provide." However, he was of the opinion that if AI was seen as a cost play, it would limit the possibilities AI presents.

Survey respondents did point out that cost was the top most challenge in adopting AI. But, it turns out, this isn't an insurmountable challenge. Gerrit van Wingerden, managing director of Tokyo-based Tora Trading Services, told EIU researchers that small firms like his had begun to use affordable open source platforms. In practical terms AI is maturing fast, has a number of business use cases and-best of all-is accessible.

ixThe study covered 203 executives around the globe. C-level executives formed 50% of the sample. The study was evenly split between financial services, manufacturing, healthcare and retail with 48% of the organizations having annual global revenue greater than US$1 billion.
xThese documents include government identification papers such as social security numbers, passports, tax filings, asset ownership, annual reports, legal submissions, media mentions, etc.

About Author

Dnyanesh Patkar- Global Head, Strategy and Innovation, Financial Services, Wipro, Ltd.

Dnyanesh Patkar has over 20 years of international experience at companies such as Alta Resources, Schneider National, Infosys, DiamondCluster and National Semiconductor. He is a seasoned general management executive recognized for creating high performance teams, leading change, and generating profitable growth. He has in depth experience in global business transformation leveraging strategy, technology and operations in North America, Europe and Asia.

Currently, Dnyanesh leads Strategy and Execution, and Innovation for BFSI, Wipro's largest SBU. Prior to Wipro, he had P&L responsibility for BU of over $50M, and delivered 12% YOY revenue growth while improving operating margin by 5%. At Schneider National, he led the Strategic planning and New Venture Innovation teams in Chief of Staff role for the CEO. He was also a core team member and change agent to drive digital transformation at the company. Earnings after program completion were 5X pre-program earnings. He has several years of experience as a management consultant working with FORTUNE 500 companies across various verticals on ecommerce carve outs, market entry strategies, post-acquisition implementations, and operational efficiency engagements.

Dnyanesh has his MBA from the Wharton School of Business, and Masters in Electrical Engineering from Cornell University. He completed the Executive Management program from Stanford University in 2014.

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