Artificial Intelligence is already changing the face of banking on a global scale. Long before chatbots popped up as interesting business-use cases, long before mobile banking applications offered military-grade secure transactions, and much before focused analytics tools for banking made themselves known, AI apps for banks augmented by machine learning and deep learning began creating an impact in the world of banking.
What makes banking a high-potential market for AI?
The question is natural, and when you think of it, this hardly comes as a surprise. The following factors make the financial sector a highly targeted market for all kinds of AI apps for banks:
- Massive amounts of structured data from the past.
- Consistency in data recording and archiving practices across financial institutions.
- Quantitative nature of finance and banking business practices and operations.
- Accuracy in data records.
Artificial intelligence is already playing a role in critical finance and banking functions such as loan approvals, asset management, portfolio design, and risk management. However, the true potential of AI apps for banks extends much beyond these often talked about areas of work. In this guide, we present a sneak peek into the fascinating forces of change that AI apps for banks and platforms are bringing into play in the world financial services.
AI apps for banks: Smart portfolio management for end users
Heard of robo-advisors? Well, the term is quite misleading in the sense that there are no robots involved. It’s pure AI-based algorithms in play, helping individuals with portfolio creation. Leading banking and wealth management organizations have invested in such AI applications. These applications help banks build an online accessible tool that considers user preferences, personal demographic information, earning power, and wealth sources, and then matches these with their financial goals. In parallel, these systems take real-time market data and factor in parameters like a customer’s credit history, risk aversion, and lifestyle practices, creating a very robust portfolio of investment and saving instruments across asset classes. By regularly measuring the success of suggested portfolios with real market forces, these algorithms keep on becoming smarter by including more factors in play. This not only helps end users quickly get vital inputs on suitable financial products, but also helps banks market and sell the most appropriate products to users.
Better product recommendations and personalized advice
Extending the idea of personalized investment portfolio suggestions, there’s the more generic concept of personalized financial advice, which is expected to go big very soon. Mobile banking applications like Moven and Simple leverage the idea of using algorithms that grow smarter with time. More precisely, they grow smarter by handling more data and measuring more results.
These AI-based applications can integrate with a user’s online bank accounts, debit and credit cards, and e-wallets to track their expenses, present advice on better expense management practices, and help them choose more suitable financial products that sit well with their financial habits, liquidity requirements, and short-term saving goals. These, and similar AI-based applications, can transform the market of financial advice and product recommendations in a big way.
Automated hedge fund management
Hundreds of hedge funds across the world are using AI-based models. These models collate inputs from several sources of real-time financial information from the major financial markets of the world. Also, these models incorporate quantified inputs regarding sentiments in the financial markets. With all these information inputs and highly sophisticated algorithms, these AI models are able to make investment decisions very quickly. Increasingly, hedge fund trading is being managed via such AI-based models, even if the strategies of decision making are different. As soon as these models identify trade opportunities, they can execute the trade, with minimal to zero user intervention. Funds like DE Shaw, Two Sigma, Winton Capital Management, PDT Partners, and Citadel are the key players in AI-powered hedge fund management.
Self-learning security systems for fraud detection
Finance and banking businesses can never really operate without having their eyes firmly glued to the state of data security and privacy. However, easily available computing power, storage of massive proportions of entire business data on public cloud, outpacing of learning in the cybercrime world as compared to cybersecurity world, all combine to create the “perfect storm” potential for these organizations.
Conventionally, complex data security rules create false positives (flagging of genuine transactions as malicious). Also, because of the incalculable number of security breach scenarios, the need for security systems that can “learn” and grow more secure is obvious. AI-based fraud detections tools of today leverage the principles of deep learning and machine learning, and are already beginning to incorporate the benefits of neural learning, and hence have tremendous potential in cybersecurity, particularly for the banking sector.
Operational productivity gains
AI apps for banks can automate routine, mundane, labor intensive and repetitive tasks in customer communication workflows and back-office operations. This makes several processes more inexpensive, productive, and quicker than ever. Also, tedious processes such as customer education, new customer orientation, and communications management become a lot more personalized and automated with the help of these AI tools. In scenarios were legal compliances, process updates, and new product designs necessitate changes on communications and workflows, AI tools help implement the changes in near real time.
Critical technological investment
With strong potential in cost reduction, personalized service delivery, and fraud detection, AI has become a critical technological investment for the banking sector. Digital consumers are constantly interacting with firms that already leverage AI applications to know them better. Their expectation and readiness to have AI-focused algorithms drive financial interactions will soon be obvious, and drive more investment and adoption in these technologies.
Already, close to 10 percent of organizations (as per a report by Narrative Science) are using AI to identify opportunities that would otherwise be missed. Very soon, financial services will recognize the dire need to adopt AI applications to deliver sophisticated, personalized, and highly secure services to clients.
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