Hyper-automation-enabling software market size is expected to grow to $596.61 billion from $481.63 in 2020. For the purpose of this insight, hyper-automation collectively refers to
Traditional large banks (such as Industrial and Commercial Bank of China, JPMorgan Chase & Co., Wells Fargo, Japan Post Holdings Co. Ltd., etc.) are diligently investing in high-tech. Under very similar precepts, community banks are focusing on mergers and also investing in high-tech to counter strong disruption currents. When speaking of high-tech, large and smaller regional community banks, alike are dedicating significant percentage of operating budget and forethought towards self-disruption. According to Statista, global IT spend in banks and Securities sector is anticipated to be 547.82 billion USD by 2021. In terms of percentage, in 2020, North American banks spend up to 40% of their IT budget in new technology and 30% in Europe.
As financial institutions deepen omni-channel reach, they have to accept their growing reliance on telecommunications to facilitate seamless service. It’s no fluke that so many legacy financial institutions are investing in state-of-the-art IT infrastructure. Responsive, agile or ‘cognitively intelligent’ network and IT operations aren’t just buzzwords. Financial institutions are conducting serious assessment of their IT department’s functional capability, operating costs, down time due to system outage and it’s impact on customer retention, impact of speed of processing transaction on customer experience, ability to mitigate
Anyone familiar with the banking industry knows that money laundering, financing of terrorist / suspicious activity issues never abate. Regulators periodically issue new decrees, introduce reforms to combat these pesky yet very expensive violations that can cost banks, anywhere from millions to billions in penalties.
Wachovia, now part of Wells Fargo was accused of facilitating Mexican drug cartels through accidental wiring of more than $400 billion over 2004-2007 period. According to original estimates by United Nations Office on Drugs and Crimes, 2%-5% of global GDP or $800 billion USD to $2 trillion is laundered each year across the globe.
Natural Language Processing (NLP) has come a long ways to be able to work with unsupervised data (self-learning) but in most industries, it is still equated to virtual assistants (‘chatbots’) that can handle basic customer queries. Most AI enabled linguistic mentions fail to distinguish between Natural Language Understanding (NLU) and Natural Language Generation (NLG) and how advanced deep learning (specifically recurrent neural networks) are ushering in a highly sophisticated era where virtual assistants go beyond answering basic customer query.
Even more amusing is how many Financial Institutions fervently market their virtual assistant as the next big breakthrough, so much so, they christen it with a human name. In this research, we move past this repeated diatribe into more sophisticated yet highly practical NLU, NLG use cases:
PayPal processes 27 million transactions per day yet records an impressively low fraud rate of 0.32%, as compared to 1.32% reported by other merchants. This has been possible due to PayPal’s long standing commitment to risk management and fraud detection. PayPal is actively deploying deep learning for customer segmentation, behavioral analysis and to analyze large volume of transactions across various parameters.
In recent times, many solution providers have claimed embedding AI in their Anti Money Laundering (AML) offerings. Upon conducting further research, it has become apparent that most enterprise level solution providers are supplementing existing AML solutions with predictive analytics, dashboard overview while very few are actually offering AI enabled real-time monitoring capability or agile solutions that respond to future anomalous behavior.