"Eighteen noteworthy applications of artificial intelligence in the banking sector worth exploring"
In the ever-evolving world of finance, Artificial Intelligence (AI) is making significant strides in enhancing customer support and combating fraud. Let's delve into some notable examples of AI implementation in the banking sector.
ING Bank leads the way with AI chatbots tailored to different markets. From Lionel in Australia to Marie in Belgium, these chatbots utilize Natural Language Processing (NLP) to understand customer needs and provide self-service options, ensuring 24/7 support on platforms like Meta Messenger. ING has also pioneered voice technology, allowing customers to log in and perform functions by voice, enhancing accessibility and customer satisfaction [1].
Bank of America's Erica, an AI-driven virtual assistant, offers personalized financial advice and customer support within the mobile app. Erica uses advanced analytics, machine learning, and cognitive messaging to interpret voice, text, and tap commands. It helps customers manage cash flow, track spending, pay bills, and receive alerts on recurring charges. Erica processes over 2 million interactions daily and resolves about 78% of customer queries within 41 seconds, significantly improving operational efficiency and engagement [2][3][4][5].
Wells Fargo's Customer Engagement Engine uses machine learning to analyze customer transaction data and spending behavior, providing personalized product and service recommendations in real-time. This enables "next best conversations" that enhance cross-selling and customer satisfaction [2].
Axis Bank’s Aha! Voice Assistant leverages multilingual NLP to support natural conversations in English, Hindi, and Hinglish. It assists customers with loan options, EMIs, credit card application statuses, and documentation needs. Aha! handles over 100,000 voice requests daily, improving service speed and accessibility [3].
These AI implementations use technologies like NLP, automatic speech recognition, machine learning, and predictive analytics to provide 24/7 customer support, enable personalized financial advice, automate routine inquiries, and improve customer engagement while reducing operational costs [1][2][3][4][5].
On the fraud detection front, companies like DataVisor in Mountain View, California, use machine learning to counteract application and transaction fraud. Vectra AI, based in San Jose, California, provides an AI-powered cyber-threat detection platform to financial institutions, helping to identify hidden attackers specifically targeting banks [6].
Feedzai uses machine learning to help banks manage risk by monitoring transactions and raising red flags when necessary. Ayasdi's AI-powered AML incorporates three key advancements: intelligent segmentation, an advanced alert system, and advanced transaction monitoring [7]. Symphony AyasdiAI, based in Palo Alto, California, is another company using AI for anti-money laundering (AML) [8].
Despite the implementation of fraud detection protocols, identity theft and fraud still cost American consumers billions of dollars each year [9]. According to UN estimates, up to $2 trillion is laundered every year, or five percent of global GDP [10].
In an effort to combat these issues, companies like Ginger offer next-day approval for non-dilutive payment solutions and turn-key integrations with clients' existing tech stack. ZestFinance's AI-based software purportedly generates fairer models by downgrading credit data that it has "learned" results in unfair decisions [11].
Upstart uses AI to make affordable credit more accessible while lowering costs for its bank partners. Kensho Technologies, based in Cambridge, Massachusetts, provides machine intelligence and data analytics to leading financial institutions [12]. FIS's credit analysis solution uses machine learning to capture and digitize financials as well as deliver near-real-time compliance data and deal-specific characteristics [13].
A study published by U.C. Berkeley researchers concluded that fintech lenders discriminate less than traditional lenders overall by about one-third, but still discriminate [14]. As the financial industry continues to embrace AI, it's clear that these technologies will play a crucial role in shaping the future of banking, fostering customer satisfaction, and combating fraud.
The PNC Financial Services Group offers a variety of digital and in-person banking services, including the corporate online and mobile banking platform PINACLE with a cash forecasting feature that uses artificial intelligence and machine learning [15].
Gynger is a B2B payments and financing solution that partners with both buyers and sellers of technology. Its underwriting is powered by artificial intelligence [16].
As AI continues to evolve, it's exciting to see how it will reshape the banking landscape, providing innovative solutions to age-old challenges and enhancing the overall customer experience.
- Fintech companies, such as Feedzai and Ginger, employ machine learning to help banks combat fraud and identity theft, while ZestFinance's AI-based software aims to generate fairer credit models.
- In the realm of technology and finance, innovation is evident in several ways, from banking AI chatbots like ING's Lionel and Marie, to data analytics providers like Kensho Technologies, all of which are shaping the future of the banking industry by enhancing customer support, fostering personalized financial advice, and reducing operational costs.
