How AI Is Making Banks Cost-Effective and Efficient


From transforming the customer experience by enabling frictionless interactions to detecting, assessing risks, preventing fraud and performing KYC regulatory checks, banks are adding intelligence atop their core systems 

The use of artificial intelligence (AI) is becoming pervasive across the BFSI industry. According to IDC, almost 85 per cent of banks implemented AI applications to enable intelligent decisions and automated processes for corporate know-your-customer (KYC) procedures, drastically reducing the time it takes to approve enrollments for new corporate accounts last year. Also, AI technologies and conversational interfaces are improving the speed of response, efficiency, and personalisation.

According to Autonomous Next research, the aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total.

The Middle East is emerging as one of the fastest-growing markets for AI in banking due to growing digitalisation in the last couple of years. Growing government policies and initiatives to increase the adoption of AI across various sectors and the adoption of innovative technology will further drive the market growth in this region.

In 2018, UAE-based Mashreq Bank implemented an automation platform that integrates emerging technologies such as robotic process automation (RPA), AI, machine learning (ML), and cognitive capabilities to deliver seamless digital customer experiences and improve operational efficiency for the bank’s staff. The custom interfaces enable enhanced human-robot interactivity and adaptive positioning technology that automates application controls and fields.

Emirates NBD started using Amazon Web Services (AWS) Ml service that enables the development of individualised recommendations for customers to help build a “bank of the future” in 2019. According to Emirates NBD, Natural Language Processing (NLP) technology helps customers interact with call centre automation in a more natural way. To support this, Emirates NBD is using Amazon Polly, a cloud service that uses deep learning technologies to convert written content into human-like speech.

In 2019, Saudi-based bank Alawwal partnered with leading global tech firm Reaktor to train its entire workforce on the basics of AI. Through the AI training, Alawwal bank hopes to lead the region’s financial services industry in adopting a technology estimated to contribute $320 billion (11 per cent) to the Middle East’s GDP by 2030.

“The bank took the unusual step of training all staff in the technology so every team could spot opportunities where AI could benefit the business and its customers,” the Alawwal Bank statement said.

Earlier in May, Qatar Islamic Bank (QIB) launched a conversational virtual assistant, armed with proprietary Al and ML algorithms. The virtual assistant, Zaki, is designed to provide relevant and contextual responses to customers’ queries. In the future, customers will  initiate transactions either through voice or chat, providing an additional fast and secure channel for banking with QIB and offering conversational AI banking. Banks in Qatar are also investing in AI-powered protections to filter malware and phishing threats. 

Some major key players for global AI in the banking industry are Microsoft, Google, Intel, Oracle, Amazon Web Services, Salesforce, SAP, Zest AI, IBM, DataRobot and Accenture among others. 

If automated intelligently, many areas in banking have a real ability to move the dial on improving both customer experience and reduce the operating cost.
Here are some use cases of AI.

Customer Service

Customer experience is at the heart of banking, and enhancing is both a challenge and an opportunity for banks. Using AI-powered tools and devices that can analyse the vast trove of data to derive customer behaviour patterns, banks can offer their customers personalised products and services. AI not only enhances customer satisfaction, but the workload has also been reduced and accumulated processed data is easily accessible.

Bots, chatbots 

With NLP evolving and domain expertise being added to AI systems, banks are increasingly using AI to automate processes, interact with customers. Bots help banks carry out repetitive tasks at the backend, such as sifting through a wide set of documents to fetch data instantly or to automate simple processes.
Chatbots help customers engage with banks any time and obtain information or help. Whether it is Bank of America’s Erica, launched in 2018, and serving 10 million users, or HSBC’s Amy or Emirates NBD’s Eva, chatbots help customers cut through time consuming layers and access the information they want immediately.

Chatbots are predicted to see a 3,000 per cent growth between 2019 and 2023. Additionally, Juniper Research suggests banks will be globally saving as much as $7.3 billion by 2023 using chatbots.

— Robo-advisers 

Starting with Betterment, the first robo-adviser launched in 2008 in the US to Sarwa, robo-advisory firm in the UAE in 2018, the first to grade from the Dubai Financial Services Authority’s regulatory sandbox, the robo-advisory market is expected to reach over $16 trillion by 2025, roughly three times the assets managed by BlackRock, the world’s biggest assets manager to date. A robo-advisor is an automated service that gives you advice on managing investments, and helps you in buying some financial products. With robo-advisory, very little human intervention is needed from registration to execution. These platforms are less expensive than traditional fund managers, they also avoid human emotions like fear and greed in investing decisions.

Fraud detection 

AI is used in the middle office of banks for fraud management. With the increasing usage of digital banking, cybersecurity and operational risks have also gone up. Banking systems use ML and Image Recognition Technologies to figure out anomalies in user behaviour and reduce fraud cases — analysing customer behaviour in real-time to determine what activity looks out of kilter. The use of AI includes analysis techniques like calculating statistical parameters, regression analysis, probability distributions and models and data matching. Some of the most popular fraud types in the banking sector are the use of false identities, money laundering, credit card fraud and mobile fraud.
The accuracy to avoid false-positives are improved over time by looking at more of the second and third-party data available to not only support in assessing valid transactions, but also make identity verification stronger through biometric-based techniques.

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Know your Customer (KYC), customer onboarding and loan processing

Customer onboarding and the KYC process is inefficient due to manual processing. But AI can help streamline it by verification techniques like facial recognition, or automation of document uploading. This is where AI-based document extraction comes into play. OCR, a software that is supplemented with the flexibility and other advantages of specifically designed AI, can be used to pre-populate application data as well as providing verification of identity. Previously, in the absence of AI, loan processing would take months. With AI, a humongous quantity of loan requests can be managed in a couple of days. 

Anti-Money Laundering (AML)

Transaction monitoring has always been a challenge for full-service banks. AI can play a vital role in addressing this long-standing pain point by helping banks move from rule-based analysis to more risk-based assessments. Advanced technologies can also increase regulatory reporting efficiency, which is a delicate point in the AML value chain. The RPA can be used to populate regulatory reporting formats with existing data and to archive reports electronically. The automated generation of regulatory reports has long been a feature of traditional reporting modules in AML tools. The limitation of these systems is the narration of the suspicious activity report written by analysts. Advances in natural language generation (NLG) allow relevant information about detected cases to be gathered in a coherent narrative that will be provided to the analyst for examination or modification, thus supporting assisted creation of reports.

Also Read: Can Blockchain Reduce Ad Fraud?

Analysing data to predict future 

AI can help banks to predict future trends and outcomes. By analysing relevant and existing data sets, banks will be able to generate intelligent recommendations of services and products to their customers, in real-time.  

Hiring professionals

Human biases interfere in the hiring process. AI hiring engines help find the best candidates for a particular job, eliminating all biases. 

AI helps banks automate their processes, making the financial institutions more efficient, cost-saving and seamless, and save human resources for more complex tasks.

The future of banking is not simply a matter of choosing a robot over a human being. Instead, value-added and personalised service delivered by bankers empowered with a broad set of AI-powered tools will help provide a superior banking experience to customers.