Banking on Artificial Intelligence

  • Mallika Rangaiah
  • Mar 06, 2020
  • Artificial Intelligence
  • Updated on: Mar 25, 2021
Banking on Artificial Intelligence title banner

With digital upheaval rapidly rippling across the world, transforming industries, and revolutionizing businesses with its power, no sector can afford to get marooned to the sidelines. As every industry engages in designing and developing approaches and methods to remain relevant in a world steered by technology, the banking sector is no exception. 

 

Customers, now familiarising themselves with advanced technologies and techniques in their everyday lives, no longer expect banks to be characterized by long queues, frequent visits, and an excruciating degree of paperwork. They need transformations and they need them fast. These digital advancements escalate the vulnerability of critical information to cyber-attacks and fraudulence, endangering both the bank’s profitability and its goodwill. 

 

Thus the government regulations imposed on banks as a result of rising security threats and the compulsion of having to maintain the capital adequacy ratio as per the international regulatory framework guidelines restricts their capacity of keeping pace with the digital advancements. This subjects them to competition by the lithe Financial Technology (FinTech) players who are not obligated to preserve the capital adequacy ratio.

 

Artificial Intelligence, as a result, becomes a powerful and handy weapon that is wielded by banks for enabling the power of digitization for banks and support them to compete with the rising competition extended by FinTech players. 

 

 

AI and Banking

 

In a promptly digitalizing world, Artificial Intelligence has emerged as the future of banking, propagating the power of advanced data analytics in order to withstand fraudulent transactions and improve deference. It incorporates deep learning, predictive analytics, as well as machine learning for enabling an enhanced banking experience.

 

AI assists in fraud detection, credit risk assessment, reduction of costs as well as risk management. Alongside these aspects, the sector is also leveraging for battling with frauds and hacks while simultaneously abiding with KYC and AML compliance regulations. In this blog, we talk about how Artificial Intelligence is being leveraged by the banking sector.

 

 

Examples of AI in Banking

 

Firstly let’s briefly brush up on our understanding of the concept of Artificial Intelligence. Artificial Intelligence, in layman’s terms, is basically the simulation or imitation of human intelligence to use it in machines and program them to think in terms of humans and to mimic their actions.

 

The term can also be applied to define any machine or software which manifests traits that are associated with the human mind. The AI algorithms can tackle learning, perception, problem-solving, language-understanding, and logical reasoning. 

 

Generally being early embracers of most new technologies, Banks leverage AI particularly in their front office (conversational banking), middle office (anti-fraud), and back-office (underwriting). 

 

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Following are some areas where Artificial Intelligence has been of prestigious value in the Banking sector. 

 

Chatbot

 

We’ve all come into contact with Chatbots at some stage or the other such as while accessing e-commerce websites while reaching out to customer support or while booking hotels or flights for travel.

 

Chatbots are AI-enabled conversational interfaces. They can handle compelling conversations on behalf of the bank with millions of consumers, at a fraction of the cost. They possess the potential of bolstering the bank’s customer experience and its convenience. 

 

With people no longer holding the time and patience to be physically present at the bank for all tasks and also in cases where the banks are closed, the chatbots step in to serve as the saviors. Their 24x7 availability and efficient customer service make them an excellent apparatus for the sector.

 

Chatbots also assist customers in seeking their transaction details and all additional services that they are eligible to receive. They are programmed to comprehend the customer’s requirements and offer them the appropriate response.  

 


The image highlights the benefits of Chatbot in the Banking Sector, from functionality improvement, time-saving, service flexibility, costs reduction to efficiency.

Benefits of Chatbots in Banking


 

 

An example of a popular AI Chatbot is virtual financial assistant Erica, introduced by Bank of America. Erica plays a crucial role in fulfilling the bank’s customer service requirements through numerous ways, be it by sending notifications to customers, providing balance information, sharing money-saving tips, providing credit report updates, facilitating bill payments, and helping customers with simple transactions, etc.

 

The chatbot’s capabilities have recently been expanded to help clients in astute financial decision making, by providing them with personalized and proactive insights.

 

 

Fraud Detection

 

Security is of paramount importance in all sectors, particularly in the case of financial institutions like Banks that face an eternal threat of frauds and hacking. Through the combined use of supervised and unsupervised machine learning to interpret insights absorbed from trends, AI helps in decreasing false-positive rates, avoiding fraud attempts, and reducing manual reviews of potential payment frauds.

 

AI is used to fend off identity theft by incorporating biometric identification systems like voice and facial recognition, into the login module for strengthening the identity verification process.

 

As technology advances so do the complexity posed by payment fraud attacks. Having a digital footprint or sequence that makes the attacks undetectable through the sole use of predictive models enhances the importance of AI as it assists in mitigating these attacks and in providing a security layer to the bank. Its prompt and large-scale detection of payment frauds makes it an excellent asset for banks in handling such cases. 

 

AI’s predictive analytics and machine learning allow for inconsistencies in large-scale data sets to be traced in seconds.

 

 

Mobile Banking

 

The option of mobile banking has been an easy and convenient resolution for the customers who no longer feel the necessity to be physically present in the bank for all menial tasks. Comprehending the extensive perks and benefits of mobile banking, users now enjoy this service owing to its safety, security, and easy access.   

 


The banner shows a glimpse of how Artificial Intelligence is being leveraged to make Mobile Banking possible

Mobile Banking with Artificial Intelligence


 

An excellent example of Mobile Banking would be Varo Money, a company that has worked diligently on reinventing banking’s approach and merging financial experiences into their users’ daily lives. Their app Varo is an intelligent mobile banking app that engages in enhancing consumers’ financial health by advocating positive spending, savings, and borrowing habits. 

 

Intelligent banking apps can provide customers with personalized insights and recommendations wherever and whenever they want. AI assists in personalizing mobile banking by offering real-time customer support through back-end use of analytics and machine learning, offering advice and personalized communications through Robo-advisors, assisting in personal planning, personal reminders, etc. 

 

 

Customer Engagement

 

Massively impacting the goodwill of any organization, customer experience is one of the most crucial aspects to be considered.  This is especially in cases of banks where 24/7 availability and swift transaction are required. AI, therefore, assists in ensuring that the banking transactions flow smoothly and effortlessly. This is done through the development of various AI-powered features such as chatbots and biometrics. 

 

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An example of one such feature is when NatWest, became the first major U.K. bank to allow its customers to open accounts remotely with a selfie. The AI-powered biometrics which the firm developed with its software partner HooYu, match an applicant’s selfie to a passport, government-issued I.D. card, or other official photo identification documents in real-time. 

 

Credit Risk Assessment

 

“Speed is of the essence in credit risk management. The earlier we detect any risk, the quicker and better we can serve clients to prevent losses. Through machine learning, the EWS scans financial and non-financial information, such as news items from all over the world.” -  Anand Autar, project leader, ING.

 

AI-driven models are capable of facilitating immediate assessments for credit risk evaluation of a client. This helps banks in providing the right offer to their customers. In the case of pricing and underwriting services, Artificial Intelligence can cut down the turnaround time and escalate the whole process. AI increases the efficiency of client proposals and boosts the overall customer experience.

 

 

Cost Reduction

 

Banks could save a humongous $447 billion by 2023 by deploying artificial intelligence (AI), as stated by AI IN BANKING research report from Business Insider Intelligence. Employment of AI allows banks the scope of cutting down on 3 main areas :

 

1. Reduces Cycle Time

 

With the automation of the digitization process, the time spent on digitizing, discovering, and onboarding document templates is reduced which allows the bank to redeploy its employees to more paramount projects.

 

2. Minimises Rate of Errors

 

The automation in banking systems allows for errors to be reduced without there being any escalation in the cost. AI system's quality of excelling at handling unstructured data awards them the advantage of lower error rates.

 

3. Solution Costs

 

As per IBM data, the traditional onboarding process for document digitization costs hundreds of millions of dollars for a single department. By leveraging AI tools that can be 80% automated and have the potential of 90% accuracy, cut down their onboarding process, putting more focus on data validation over physical presentation and scanning. This would help curtail error rates while also making more competent use of employee effort.

 

 

Cutting down operational costs

 

AI's use in banking can cut down the errors which can be associated with human manual processes such as data entry and paperwork. Techniques like discovery and process mining can be used for simplifying human tasks through Automation bots (RPA), AI assistants, and computer vision

 

AI tools collect data, classify, and execute it in the absence of any human intervention. For instance, it can be adopted for scanning an email inbox for invoices, for discovering relevant text in the data, for inputting text within a system, reviewing the data, and for making decisions. 

 

AI-powered machines will enable humans to spend their time on more critical tasks, with computers handling manual and repetitive activities. This results in more satisfied staff and customers.

 

 

Compliance

 

A pivotal role in the banking system is played by Regulation. AI contributes by offering complex data analysis, automation of manual compliance processes like "Know Your Customer" (KYC).

 

Both of these processes rely on gathering data from various systems to understand the customer and transactional behaviors. In the absence of AI, it may be highly time-consuming, leading to a delay in the service offering to the end-user.

 

Data is swiftly integrated by AI algorithms through multiple systems in real-time, both efficiently and precisely. Through different rule sets, the machine learning models can examine behavior patterns and decipher the possibilities of risk the bank is exposed to. Humans generally take a long time to execute tasks that can be carried out in seconds by machines.

 

 

Conclusion

 

I would conclude by stating that while AI and ML have the power to continue to hugely offer an edge to the banking industry, yet the technology’s full potential can only be experienced if there is full infrastructural support. As banks increase their reliance on ML to provide predictive analytics, they will need to meet new regulatory and interconnectivity demands. 

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Comments

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    Oct 28, 2020

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    Oct 28, 2020

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