Fancy an automated financial adviser to make investment decisions an easy and rational affair? How about a smart wallet that helps alter your spending and saving behaviors? From insurance underwriting and banking to wealth management and trading, data-driven applications based on artificial intelligence (AI) are creating a buzz in the finance industry. Yet, financial institutions are treading with care.
According to a recent report, the AI industry will be worth $14.2 billion by 2023, up from $525 million in 2015. The disruption is already being felt across industries – healthcare, agriculture, manufacturing, customer service management, travel, hospitality, automobile, and just about everywhere. However, despite promises of exceptional speed, accuracy and cost efficiency, the finance industry seems cautious about embracing technologies such as natural language processing, deep learning and machine learning. Why is it so?
Before we answer that question, let’s first understand AI; because most enterprises are still unsure about what AI can and cannot do, thanks to all the hype surrounding it. By definition, AI is the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. While the technology, in its simpler forms, has been around for decades, what’s new is the promise of extracting valuable information that can enable enterprises to take calculated, strategic risks and explore brave, new opportunities.
Even so, why is AI in finance a daunting proposition?
The potential: According to a study, by 2020, customers will have 85% of their business interactions with no human communication. Chatbots, powered by natural language processing (NLG) and machine learning algorithms, will become the norm. In fact, an increasing number of enterprises across the globe, in the banking, financial services and insurance sector are already adopting AI-powered chatbots as virtual agents to deal with the mounting number of customer queries.
The pitfall: Normally, chatbots don’t perform any critical tasks. They may help financial enterprises to cut costs, but they don’t really improve the customer experience. Experts concur, that to be truly effective, chatbots must be able to understand not just what the customer is saying, but also the intent, and then, respond accordingly in real-time. Until that happens, chatbots delivering a personalized customer experience will remain a far-fetched dream.
Rooting for robo-advisors?
The potential: There’s something very enticing about digital platforms that provide automated, algorithm-driven financial planning services. Robo-advisors can monitor the markets round the clock and offer cost-effective portfolio optimization. AI-based systems analyze huge amounts of data – market prices, volumes to macroeconomic data, corporate accounting documents, etc – to arrive at predictive analysis and investment strategy. With machine learning, it’s possible to formulate a data-based method to trading stocks.
The pitfall: Most robo-advisors only allocate their clients to exchange-traded funds (ETF) portfolios. AI does not really excite traditional traders in the stock market, primarily because of the lack of transparency in the algorithms. Besides, stock trading is not always impacted by expected and predictable parameters. Although AI can offer invaluable insights and suggestions, it can’t replace the human element. Not yet, anyway.
The potential: When it comes to security and fraud detection, AI – armed with advanced learning algorithms and cognitive analytics – can offer accurate, dynamic and robust solutions that are far superior to what is deployed in traditional financial institutions.
The pitfall: Applying AI for fraud prevention and detection is far from simple. The rules of the game keep changing constantly. Besides, when things go wrong – say, AI applications are attacked and cyber crimes committed – the effects can be devastating, especially in the case of financial institutes. There is too much at stake.
Personalization v/s privacy concerns
The potential: Just like algorithms track your online habits to create personal virtual experiences, the financial sector can track your activities, behavioral characteristics and financial data to offer personalized services. How about your own digital personal financial assistant?
The pitfall: The data used (with or without your knowledge) to offer personalized advice and recommendations can be considered an invasion of one’s privacy. The associated legal and ethical problems are major deterrents, especially for the financial sector that’s governed by strong regulations. Unless new regulations are in place, it will be difficult for financial institutions to jump on the AI bandwagon.
All things considered
AI has immense potential to disrupt every industry, including the financial sector. However, there’s a long way to go before AI-based systems become sufficiently advanced, affordable and secure to replace the current norms in financial institutions. From streamlining internal processes to improving customer experience, AI in finance holds the promise of a better future.
For now, though, it’s best for the finance industry to take baby steps into the big, bold world of data-driven technology; and assess how it will change the role of human beings in the realm of smart machines.
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