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6 Ways Automation In Banking Can Change The World

By Vinayak Kumar
Banks around the world are utilising automation to adapt to organisational and economic shifts, reduce risk, and provide innovative customer experiences.

Nearly every industry, including banking and finance, has experienced significant changes due to artificial intelligence. Methods that are less resource-intensive, more convenient, and efficient are driven in large part by automation.

Banks around the world are utilising automation to adapt to organisational and economic shifts, reduce risk, and provide innovative customer experiences. Approximately 23% of banking firms have already integrated artificial intelligence into their operations and products/services.

Optimising banking operations is imperative because of rising customer expectations, stringent regulations, and increased competition. At Growth Jockey, we assist businesses in understanding the value of digital technologies and help them thrive in this new, technology-driven corporate world.

By doing so, we help financial entities to automate their business models for increased productivity.

Automation in banking

The banking industry relies heavily on accurate, precise, and quick processes. The automated teller machine (ATM) became famous in 1969 and is one of the banking industry's most well-known examples of automation.

Mobile banking has also emerged due to the proliferation of smartphones and other cutting-edge devices. Customers can easily view their balances, make bank transfers, and get answers to their questions.

Client requirements are met through banking automation, which also benefits financial organisations by freeing up resources for other core functions. Machine learning (ML) and Artificial Intelligence (AI) can significantly enhance automated banking services.

But how do banks automate their operations? It involves a holistic strategy to automate day-to-day activities at any financial institution.

Process of automation in banking and financial services

A strategic transformation that delivers the full benefits of automation should be based on a strategic rather than a tactical approach:

Create an end-state comprehensive AI strategy

Keeping the organisation's goals and values in mind, AI implementation begins with creating an enterprise-level AI strategy. To start, there should be internal market research to identify AI technology-capable personnel and procedures.

Further, checking whether the AI strategy complies with the rules and standards of the industry is vital. Finally, refining the internal practices and policies around data, infrastructure, and algorithms to form an AI strategy is done.

This will give the bank's various functional units clear directions and guidance for implementing AI.

Plan a research-backed process

Next, banks must assess the necessity of incorporating AI banking solutions into their existing or altered operational procedures. While some may be necessary, others may seem redundant. Further, the technology teams should check the testability of the AI and machine learning use cases that have been identified for banking.

It is crucial to investigate every aspect and locate implementation gaps. The planning stage finishes with the mapping of the AI talent. Banks can hire software engineers or information researchers to create and execute computer-based intelligence arrangements.

Cross-functional collaboration with a technology provider or outsourcing can be effective if banks lack in-house experts. At Growth Jockey, we perfect the combination of brilliant minds and reliable technologies. Our unique, individualised solutions help businesses find opportunities by applying the appropriate framework.

Produce prototypes and implement the process

After planning the process, it's time to construct prototypes before developing fully-fledged AI systems. Banks must gather relevant data and feed it to the algorithm to test the prototypes. Using this data, the AI model learns and develops.

Banks must test the AI model to interpret the results once trained and prepared. Consequently, the development team can better understand the model's performance in the real world. Production data starts coming in once the system is set up.

Banks can frequently update and improve the model as more data come in.

Operate and Observe for further development

The implementation of AI banking solutions necessitates constant calibration and monitoring. Banks must create a review cycle to monitor and evaluate the AI model's operation thoroughly. They can manage cybersecurity threats better and effectively carry out their operations.

Pro-tip: At the operational stage, the continuous flow of new data will impact the AI model. Hence, banks should ensure that they log in accurate and high-quality data.

Six major banking automation benefits

There are six ways of automation in banking that have driven an optimised customer experience. On the other hand, financial institutions will successfully generate high revenue at reduced costs and time. Thus, efficiency and efficacy are banking automation's two most important benefits.

Let us look at some significant advantages that drive any successful business-

Cybersecurity and Fraud Detection

Users use apps or online accounts daily to pay bills, withdraw money, deposit checks, and do much more. This results in a vast number of digital transactions. As a result, the banking industry must increase its cybersecurity and fraud detection efforts.

In 2020 the FBI reported that Americans lost more than $54 million in phishing scams that year. Javelin's 2021 Identity Fraud Survey published that $13 billion were lost due to identity fraud. Artificial intelligence in banking comes into play at this point.

Artificial intelligence can assist in working on online money security, tracking escape clauses in their frameworks, and limiting chances. It can distinguish fake exercises and clients. Research suggests that 64% of financial institutions believe AI can get ahead of fraud before it happens.

Moreover, banks can also respond to potential cyberattacks before they affect employees, customers, or internal systems using AI's continuous monitoring capabilities in financial services.

Customer satisfaction

Customers are always looking for more convenience and a better experience. Customers use essential services like ATMs to deposit and withdraw money even when banks are closed. Automation in banking has also allowed people to access their bank accounts through mobile phones from the comfort of their homes.

Incorporating artificial intelligence into financial services will further enhance the customer experience and user convenience. Additionally, customers can access financial deals and new products anytime, anywhere.

Furthermore, AI banking aids in the precise capture of client information, facilitating error-free account setup and ensuring a smooth customer experience.

Chatbot support

Chatbots are one of the best examples of how banks use artificial intelligence for optimised service delivery. They can work around the clock once deployed, unlike humans, who have set hours.

They also keep learning about a particular customer's usage patterns. It assists them with figuring out the prerequisites of a client in a professional way. Customers will always have access to them by incorporating chatbots into their banking apps.

Besides, by grasping client conduct, chatbots can offer customised client assistance and suggest reasonable monetary administrations and items. For the banking sector, banks will save 826 million hours through chatbot interactions in 2023, according to Juniper Research.

Consequently, banks also save money. Growth Jockey can aid you in saving your time and money by reducing investment up to five times. We do this by integrating AI and hyper-automation technology to improve their business operations.

Lending Decisions

Banks have begun incorporating AI-based systems to make loans and credit decisions that are more profitable, safer, and well-informed. Many banks only use credit history, credit scores, and customer references to determine a person or business's creditworthiness.

However, these credit-reporting systems frequently contain errors, misclassify creditors, and fail to include real-world transaction history. An AI–based loan and credit system can assess creditworthiness by looking at a customer's behaviour and patterns.

Additionally, the system notifies banks of specific actions that may increase the likelihood of default. On the other hand, AI-based software has the potential to speed up loan disbursement approval processes.

Compliance with regulations

Banking is one of the world's most tightly regulated industries. Governments use their regulatory authority to ensure that banks have acceptable risk profiles and that customers are not using banks to commit financial crimes.

However, manual risk profiling takes much time and requires vast amounts of money. Since banks need to update their processes and workflows as per the regulations constantly, banking automation can optimise the process.

AI utilises deep learning and NLP to speed up and improve the operations of a compliance analyst.

Robotic Process Automation (RPA)

RPA algorithms automate time-consuming, repetitive tasks to improve operational efficiency, accuracy, and cost savings. Additionally, this lets users concentrate on more involved, more intricate procedures.

Utilising cutting-edge digital technologies provides a plethora of opportunities for financial debt recovery. By 2024, the market for debt collection software is expected to reach USD 4.6 billion. It is anticipated that the global adoption of digital debt collection services will rise during the forecast period.

Why? Due to the growing requirement for self-service payment models. It would accelerate the receivables management process and automate the FinTech debt collection.

Side note: While AI does the same optimisation for the banking industry, RPA collaborates with people by automating repetitive processes (attended automation). On the other hand, AI is viewed as a type of technology that can replace human labour and automate everything (unattended automation).

AI uses unstructured inputs and creates its logic, whereas RPA uses structured inputs and logic.

Bottom Line

The world is rapidly driving towards exponential digital change. Banking plays a significant role in all primary operations, from running a business to maintaining a household. Banks can implement and integrate intelligent automation tools by adopting a comprehensive strategy and upfront defining success.

The banking and financial sector is constantly battling to reduce costs and increase productivity. This necessitates a quick-track strategy that makes compliance management and operations systematic possible.

To provide banking services to customers that are both competitive and dependable, reliance on RPA and AI is inevitable. At Growth Jockey, we assist in creating frictionless ways of automation for optimised customer experience.

It helps to maintain regulatory pressure and bridge gaps in the system. Finally, embracing innovation will allow the banking sector to survive and thrive in a crisis.

At Growth Jockey, we are committed to delivering tailored marketing automation solutions that effectively address the critical challenges faced by our clients across various industries. Regardless of the scale of your business, whether you're a small-scale enterprise or a large corporation, you can now harness the benefits of advanced automation technology.

Take a proactive approach to unlock the next level of growth for your brand by reaching out to us today!

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3rd Floor, GJPL, Time Square Building, Sushant Lok, Gurugram, 120009
Ward No. 06, Prevejabad, Sonpur Nitar Chand Wari, Sonpur, Saran, Bihar, 841101
Shreeji Tower, 3rd Floor, Guwahati, Assam, 781005
25/23, Karpaga Vinayagar Kovil St, Kandhanchanvadi Perungudi, Kancheepuram, Chennai, Tamil Nadu, 600096
19 Graham Street, Irvine, CA - 92617, US