Edge computing with AI brings real-time insights to banking

In the banking industry, edge computing is becoming an influential ingredient for mission-critical infrastructure. Combined with AI, cloud, and 5G, the potential of edge computing in finance is nigh endless.

The improvement of customer experiences is becoming a priority in most industries today, most especially in those where customer service is essential, such as in the banking and financial services industry (BFSI).

The banking industry, particularly, has been an early adopter of new technologies — and also the sector with the most use cases. With more features being enabled by technology, banks are hoping to strengthen the overall experience to meet and even exceed customers’ increasing demands and expectations.

Blockchain, artificial intelligence (AI), machine learning (ML) and application programming interface (API) technologies are revolutionizing banking with new tailored customer experiences, hyper-personalization, and new business models.

While some edge computing is being applied today, the global edge computing market, valued at US$3.5 billion in 2019, could reach US$43.4 billion by 2027.

With the continued use of IoT, Gartner predicts that 75% of enterprise-generated data will be created and processed outside the traditional data center or cloud by 2025.

Adding to that, an IBM report states that 84% of executives involved in their organization’s edge computing strategies expect edge applications to positively impact operational responsiveness.

Adopting AI in banking

According to Sourav Bose, VP, Group Enterprise AI at UOB, the adoption of AI in banking has become more mainstream, with the deployment of such solutions making banks competitive and generating positive ROI across business segments.

For example, AI is used for digital wealth management with a bot adviser that can upload thousands of possible adviser-client interactions.

Using algorithmic trading on trade execution without human intervention, banks will have advantages related to trading executed at the best possible prices, accurate trade order placement, and reduced risks of manual errors based on emotional and psychological factors.

AI can be used to detect fraudulent transactions and anti-money laundering. This can even be applied to large data sets based on the customer’s past behaviors and other data.

When it comes to cybersecurity, AI enhances existing capabilities, improves monitoring, and implements stronger preventive protocols to guard against sophisticated threats.

While AI has been reshaping banking, there are still some challenges that need to be addressed, such as scalability issues between legacy and core systems as well as long testing times.

As most banks have invested heavily in legacy infrastructure, they would prefer to use technology that can support or scale with their existing infrastructure.

With data trapped in silos, it becomes harder to integrate with external sources. This can lead to longer times, which can lead to high error rates and poor refresh rates.

Generating real-time insights 

“What is lacking now is real-time alerts. Banks are working now on very close to near real-time alerts — and this is where edge-computing comes in. It helps banks achieve the future trends of banking, allowing us to have an omnichannel experience,” said Bose during his presentation at the AI Accelerator Institute summit.

Bose pointed out that edge computing allows both the retail banking and alternative investment sectors to reduce costs and maximize revenue generation.

Edge solutions have autonomous management, allowing a single administrator to manage deployments to thousands of endpoints. Management tasks will be carried out based on intent, with no intervention needed.

Some of the edge computing and AI use cases in banking include:

• Hyper-personalization – Bots using natural language processing to interpret and comply with customer information requests as well as perceive basic human emotions and adjust behavior accordingly.
• Retail banking – features like SmiletoPay, whereby a user just smiles to a camera, the AI captures your features and can complete your transactions. This can be applied to retail stores, etc. Edge allows seamless integration with non-banking apps, facial recognition for frictionless payments, and more.
• Corporate banking – customized lending solutions for loans based on microexpression analysis to review loan applications. The entire process is service by an AI-powered virtual adviser.
• Banking security – edge computing delivers low latency analytics that guarantees data sovereignty and security.
• Cybersecurity – real-time geo-location tagging, digital footprint analyzer, suspicious beneficiary detection, microexpression analysis for facial expressions are just some of the use cases banks can consider.

Managing the workforce  

One of the biggest problems is the shortage of skilled professionals in managing these new technologies.

Banks, which normally focus on hiring accountants, financial advisors, and such will now need to look towards building a bigger IT workforce to support these new applications.

At the same time, existing bank employees need to see how they can work best with the new tools. For example, if a customer can apply everything from a car loan to credit cards online, financial advisors will need to see where else they can utilize their skills.

Banks are aware of this, and already looking to see how they can reskill or upskill employees. However, this will not be a hindrance for them in upgrading their digital investments, especially with the competition only getting stronger by the day.

 

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