The aggregate potential cost savings for banks from AI applications is staggering ($447 billion by 2023). The Autonomous Next Research also points out that 80% of the top banks surveyed are highly aware of the potential benefits that AI brings across the maturity use cases – from Conversational Banking (AI biometrics tech. and personalized insights) to Anti-fraud & Risk (KYC and Anti-ML) to credit underwriting.
The hype around AI in Banking will only accelerate in 2021. Banks will use more of it to transform customer experience by enabling frictionless 24/7 customer interactions. In fact, more than front office applications, AI is seriously permeating into investment banking and other financial services. Many banks however have struggled to move from experimentation around select use cases to scaling AI technologies across the organization.
Let us understand what differentiates an AI-First financial organization.
The short answer is that an AI first financial institution is optimized for operational efficiency through automation of manual tasks and the augmentation of human decisions by advanced algorithm engines across diverse banking operations. The longer answer can be understood through the example below.
AI first banks embed three distinct features as part of their customer journey maps: Intelligent (recommending actions, anticipating, and automating key decisions), personalized (relevant and timely) and truly omnichannel (seamless across physical and online platforms).
For instance, a typical customer journey powered by AI will include seamless integration with nonbanking apps, facial recognition for frictionless payments, analytics backed personalized offers, customized money management solutions, banking insights based on daily patterns, and savingsinvestment recommendations basis personal history and future needs. Like preceding years, the AI focus in 2021 would accelerate in the following areas,
- Fraud Detection: AI is used to look and understand how customers use their apps, where and how it is being used, and typical interaction behaviours to analyse patterns and detect potential anomalies and possible frauds in real time.
- Enhancing online interactions through Natural processing languages (NLP): Making conversations more intelligent by studying language patterns (and recognizing emotions). This is then fed to create working conversational models that enhance customer confidence.
- Leveraging customer knowledge base: From face detection to real time cameras in ATMs to numerous decision- making / enablers for business models, AI use cases are proliferating Banking operations at a high speed. This cognitive process automation secures ROI, reduces costs, and ensures higher quality for routine operational tasks.
The mentioned applications and possibilities are not futuristic. In fact, today’s banking leaders are raising the bar by streamlining their capability stack for value creation Accelerate the next!
In 2021 and beyond we will have more examples of AI ready banks reimagining their customer engagements through AI powered decision making made possible through changes to their core technology, data, and operating model.
Views expressed in this article are the personal opinion of Pankaj Upadhyay, Vice President – Data Science, BI and Advance Analytics, Maveric Systems.
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, Pankaj Upadhyay, Vice President – Data Science, BI and Advance Analytics, Maveric Systems writes how AI ready banks will reimage their customer engagements through AI powered decision making.){kind=link}