Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to "predict the future through analysing the past" – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans. Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new "alternative" data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.
Everest Group recently conducted a study with 55 banking and financial services firms to evaluate their digital capabilities in areas including strategy, organization and talent, process transformation, technology adoption, and innovation. Here are the primary insights we collected from that study. More than 60 percent of BFS firms have invested in exploring the various use cases in cognitive- and AI-driven technologies. Typical use cases include helpdesk automation using chatbots and other cognitive capabilities for functions such as sales & marketing, data entry, credit assessment, and information gathering. BFS companies are increasingly leveraging AI-enabled transformation in areas where there is significant customer interaction.
Firms' reconciliation and exceptions management processes are manually intensive, expensive and prone to error. With rising compliance costs and greater competition narrowing margins in financial services, firms are looking to streamline their reconciliations processes through automation, giving them the opportunity to reduce the number of exceptions they manage and the time it takes to deal with them. Financial institutions are adopting emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) in an attempt to automate processes and activities that previously required human intervention. Through automation of their reconciliations, firms are seeing an opportunity to reduce operational risk and boost their overall financial position, both in terms of reduced losses and regulatory capital. How can firms embrace AI to modernize their reconciliation processes for a better operational and financial outcome?
Financial crimes continue to plague the global economy. As nefarious actors become smarter, the costs of money laundering and associated crimes has reached the trillions. At the same time, the disparities between those who can access financial services – and those who cannot – threatens to widen. To help solve these gaps, IBM Research is building new AI and data encryption tools to help keep data safe, cybercrime at bay, and make financial services more accessible. The onus to spot financial crimes falls on banking and financial institutions, who can face enormous fines for compliance failures and failing to detect, report and pre-empt criminal activities.
Many banks and financial firms are investing in AI and seeing positive return from applying AI throughout their operations. AI-based systems are helping to make more informed, safer and profitable decisions. However, with any technology that's used in a heavily regulated industry there are challenges and pushbacks to adoption. Kumar Srivastava, VP of Product and Strategy of BNY Mellon recently shared with the AI Today podcast insights into AI adoption at the bank. BNY Mellon has a Silicon Valley based Innovation Center that aims to help bring AI innovations to the bank.
Since the inception of Artificial Intelligence, humans have been curious about its usage as a digital medium to every problem at hand and the banking sector is no different. A chatbot is a computer program based on Artificial Intelligence, to provide solutions or responses to requests or questions by the customers. But, unlike'Live Chat', chatbots are fully automated. Chatbots can be programmed back and forth to streamline and optimize banking services. The paradigm of banking services has been revolutionized by technologies like AI and Machine Learning, due to the faith of average customers to let the computers, decide for them, their important financial decisions.
Mitul Tiwari expertise lies in building data-driven products using AI, machine learning, and big data technologies. Previously, he was head of People You May Know and Growth Relevance at LinkedIn, where he led technical innovations in large-scale social recommender systems. Prior to that, he worked at Kosmix (now Walmart Labs) on web-scale document and query categorization, and its applications. He earned his PhD in Computer Science from the University of Texas at Austin and his undergraduate degree from the Indian Institute of Technology, Bombay. He has also co-authored more than 20 publications in top conferences such as KDD, WWW, RecSys, VLDB, SIGIR, CIKM, and SPAA.
In China, platforms and services like Alibaba's Alipay and Tencent's WeChat Pay have brought facial recognition payments to online and brick-and-mortar retail stores. But as biometrics and facial recognition technologies become mainstream, experts and regulators are concerned about the privacy and cybersecurity risks associated with these, according to a report by Abacus. Li Wei, director of the technology department of the People's Bank of China, said consumers should realize that when they are using these features, they are giving up privacy for convenience. Faces are very sensitive personal information, and it could have a critical impact on someone if it were leaked or stolen. While people can put their bank cards in their pockets, faces are out in the open all the time, Li said, adding that some companies have not considered these issues.
The spending on Artificial Intelligence is expected to reach $57.6 Bn by 2021. Additionally, the current adoption of fintech is estimated to be at 33 percent around the world. It's no surprise that IoT devices, in conjunction with data-fueled AI systems, have the groundbreaking potential for all industries, including fintech. With everything getting digital and automated, the finance and banking sector is set to radically change by the combined effect of machine learning and the Internet of things. To gauge the scope of potential, let's look at some interesting ways how ML and IoT are transforming the fintech space.
Executives are excited about artificial intelligence. According to Venture Beat, 90 percent of C-suite executives say that AI is the next technological revolution. Financial services firms have already embraced AI for risk assessment, customer care and cognitive digitization, but Forbes notes that 51 percent of companies cite cost reduction as the primary benefit. How do financial firms leverage AI in banking to create significant cost takeout? Big data poses big challenges for financial firms.