Unveiling the Role of Artificial Intelligence in Banking Efficiency and Customer Service

In 2016, AlphaGo, a machine, defeated 18-time world champion Lee Sedol in the game of Go, a difficult board game that requires intuition, ingenuity, and strategic thinking—all of which have long been regarded uniquely as human qualities. Since then, artificial intelligence (AI) technologies have evolved much further, and their revolutionary impact is becoming more apparent across industries.

 

 AI-powered algorithms are recommending digital content based on individual tastes and inclinations, developing clothing lines for fashion merchants, and even outperforming expert doctors in cancer detection. For global banking, McKinsey believes that AI technology might potentially generate up to $1 trillion in added value per year.

 

Many banks, however, have struggled to transition from experimenting with specific use cases to using AI technologies across the organization. The reasons include a lack of a clear AI strategy, an inflexible and investment-starved technology core, fragmented data assets, and outdated operating methods that impede collaboration between business and technology teams. Furthermore, various trends in digital engagement have intensified during the COVID-19 epidemic, and big-tech corporations are looking into financial services as the next adjacency.

 

Evolution of AI in Banking Sector

 

Artificial intelligence is revolutionizing the financial business, with far-reaching repercussions for both traditional banks and neobanks. This move from traditional, data-driven AI to advanced, generative AI results in unprecedented efficiency and client engagement in the banking sector.

 

AI's role in banking began with job automation and data analysis, but has since grown to include sophisticated applications in risk management, fraud prevention, and personalized customer service. The development of generative AI, which is capable of creating and predicting based on large amounts of data, is a significant shift that promises to further transform banking operations and strategy.

 

However, these aren't the only ways AI will help banking. Traditional banks have always prioritized security, process organization, and risk management, but consumer involvement and satisfaction have been lacking until lately.

 

The use of artificial intelligence in the banking industry has considerably improved customer experience. AI-powered technology, particularly chatbots and advanced analytics, have transformed how banks connect with their customers, providing previously unavailable levels of customisation and response.

 

Many banks now use AI-powered chatbots for customer care, delivering quick responses to consumer requests and round-the-clock assistance. Since its inception in 2018, Bank of America's AI chatbot Erica has received more than 1.5 billion interactions. It offers 24/7 customer service, effectively handling queries and transactions, resulting in shorter wait times and higher client satisfaction. 

 

Also Read | Ethical and Regulatory Issues with AI: Explained

 

What barriers impede banks from using AI capabilities at scale?

 

Incumbent banks face two sets of goals that, at first appearance, appear to be incompatible. On the one hand, banks must match the speed, agility, and adaptability of fintechs. On the other hand, they must maintain the scale, security standards, and regulatory obligations of a traditional financial-services firm.

 

Despite billions of dollars spent annually on change-the-bank technology programmes, few banks have been successful in disseminating and growing AI technologies throughout their organizations. The most common barrier to banks' efforts is a lack of a clear AI strategy. Many banks have two additional challenges: a weak core technology and data backbone, and an antiquated operating model and people strategy.

 

Built for stability, banks' fundamental technology systems have worked well, notably in sustaining traditional payments and lending operations. However, before banks can adopt AI technologies on a large scale, they must address certain shortcomings inherent in traditional systems. First and foremost, these systems frequently lack the power and flexibility necessary to support the varied computational requirements, data-processing needs, and real-time analysis that closed-loop AI applications require.

 

Core systems are also difficult to modify, and their upkeep necessitates large resources. Furthermore, many banks' data reserves are scattered across various silos (different business and technology teams), and analytics efforts are mostly focused on stand-alone use cases. Without a centralized data backbone, it is nearly difficult to analyze relevant data and create intelligent advice or offer at the appropriate time. If data is the bank's primary raw material, it must be managed and made available securely in a way that allows for the analysis of data from internal and external sources at scale for millions of clients, in (near) real time, at the "point of decision" across the organization. 

 

Use Cases of AI in Banking

 

AI technologies have become vital tools in many industries, including banking. With the increasing amount of data and the need for better customer experience and security, artificial intelligence is used in banking to streamline operations and enhance services. 

 

Here are some of the applications of AI in banking:

 

1. Security and Fraud detection

 

Banks are subject to a variety of cyber threats, which AI-powered cybersecurity solutions can help detect and avoid. AI and machine learning algorithms can process massive volumes of data, recognise patterns, and identify questionable transactions or behavior. The banking and finance sectors can avoid fraudulent operations, protect consumer data, and retain their reputation by incorporating AI-based fraud detection systems.

 

2. Experience with clients

 

AI tools can also help improve the consumer experience at ATMs. Customers can have a more natural and accessible transaction experience by combining speech recognition and text-to-speech technology. The option to utilize voice to connect with the ATM can make the procedure faster and more efficient, especially for consumers with impairments or those who want a hands-free experience. Additionally, text-to-speech technology can help customers with visual impairments browse menus and complete transactions. As a result, banks may see greater client loyalty, brand recognition, and, ultimately, revenue growth.

 

3. Chatbots

 

Chatbots are AI-powered assistants that can respond to consumer inquiries, offer account information, and walk them through various tasks. Chatbots use natural language processing to interpret and reply to consumer concerns in real time, increasing customer engagement and happiness. Customer service workers can use AI to operate more efficiently and better serve their clients.

 

4. Loan and Credit Decisions

 

AI in banking is used to analyze customer data and determine creditworthiness. By analyzing a customer's credit history, income, and other characteristics, AI algorithms can assist banks and financial institutions in making informed judgements about loan approvals and credit limits. This can lower the likelihood of default, raise client expectations, and streamline the financing process.

 

Predictive analysis, on the other hand, allows banks to detect possible risks and opportunities, make educated decisions, and optimize their operations. Predictive analysis can help banks increase their profitability and competitiveness.

 

5. Process Automation

 

Banks may utilize AI and machine learning techniques to automate a variety of tasks, including customer onboarding, KYC verification, lending and credit determinations, and account opening. Automating these operations allows banks to decrease manual errors, increase efficiency, and save money.

 

Also Read | Top 10 Ways to Use AI in Customer Service

 

Conclusion 

 

The successful use of AI in banking necessitates a deliberate and strategic strategy that takes into account both the needs of the business and its consumers. Looking ahead, AI has the ability to revolutionize the financial business even more. AI is predicted to have a substantial impact on the creation of more personalized banking services. Banks may use AI to better understand their clients' requirements and preferences, allowing them to adapt their services to each individual. This could result in more personalized product options, pricing, and marketing messaging.

 

The battle against financial crime is another area where AI has the potential to have a significant impact. AI can analyze massive volumes of data to spot patterns of fraud and money laundering, allowing banks to avoid financial crime before it occurs. Furthermore, AI can assist banks in meeting increasingly complicated regulatory requirements by automating compliance operations and spotting possible risk areas.

 

AI is also predicted to play an increasingly important role in the creation of new financial products and services. By analyzing enormous amounts of data, artificial intelligence may assist banks in identifying new market opportunities and developing novel solutions to suit client demands.