Digital Banking Made Easy with AI

Today the banking industry and the broader financial sector are facing a profound transformation, enhanced by next-gen technologies. AI, machine learning, advanced analytics, and more are already on the radar of many financial services providers. Adopting these enables a redesign of the operating model, helps reshape legacy systems or decode knowledge-driven opportunities hidden in already owned data stores. Ultimately, next-gen technologies propel the financial service beyond its historical function to a standard of flexibility and personalization that digitally savvy customers demand. Such shifts become essential as new layers of disruption continue to be added by non-financial, digital-only entrants (fintech) that compete in the banking market with new value and lower costs.

According to Deloitte, most of the banking and financial sector executives that have infused AI into their core business report a positive impact on their key strategic indicators: they grow revenue, reduce costs quicker, increase operational efficiency, and improve customer engagement and experience.

While there is a long way to go, many financial industry players are already taking significant steps to become fully fledged digital organizations. Personalized lending or credit scoring systems are only a few of the functions that can be automated through technology. Read more in the article below, as we further analyze these use cases.

Table of Contents

• How can AI Transform the Financial Ecosystem?
• What Might AI Change in Back-Office Processes and Improve in Customer Experience
• What Obstacles do Banks Face in Deploying AI capabilities ?

How Can AI Transform the Financial Ecosystem?

AI helps banks automate processes and plays a key role in the customer journey. Forbes predicts that by 2025 it will be vital for banking institutions to provide seamless physical and digital interactions. To live up to the standards, introducing AI into current processes and upgrading systems using the latest technologies are necessary. There are quite a few use-cases where AI can have a real impact:

• Credit scoring and underwriting – AI models can be used in banks to create a more complete customer profile by analyzing large amounts of data and making a more precise credit risk evaluation. Another advantage of these algorithmic methods is that they enable a faster loan application process and help with an increasing number of customers.

• Customer satisfaction – One advantage that AI has over human abilities is that algorithms can ingest and analyze enormous quantities of data from a variety of sources. With customers expressing their feelings about their experience using banking services on different digital platforms, having an AI always listening to their feedback can be a crucial help for banks to improve their offerings.
• Fraud detection – With an increasing number and diversity of banking offerings going digital, security is one of the prime concerns. Profiling approaches that can learn specifics about customer’s behavior, as well as anomaly detection solutions, already have very successful use cases to show for improving security and reducing manual reviews of potential payment frauds.

What Obstacles do Banks Face in Deploying AI capabilities?

Deloitte surveyed over 1,000 executives from US-based companies that are prototyping or implementing AI. The respondents highlighted a shortage of specialized skill sets required for building and rolling out AI implementations:

• Software Developers: 16 – 34%
• UX Designers: 22 – 41%
• Transformation Experts: 27 – 22%
• Data Scientists: 27 – 30%

This is where software companies join the digital marathon and start helping organizations
prepare for AI adoption. With an agile state of mind, the two companies can help
organizations build digital lending solutions tailored to their needs to improve operations
and meet the evolving demands of the digital environment.