technology

AI in Banking : Modernizing Technology with a Futuristic Approach

In this blog, we have discussed the impact of AI in the banking sector and how banks can modernize the technology to become AI-first.

ai in banking

The most demanding and fast-paced sectors now heavily depend on artificial intelligence. By dramatically raising productivity, machine learning (ML) along with artificial intelligence (AI) has helped achieve several amazing successes.

In this quick-paced digital age, several industries are successfully utilizing the services provided by artificial intelligence companies to boost corporate development, profitability, and sustainability. Among these, digital transformation in the banking sector is a well-known one with undeniably enormous potential for AI.

Having said that, the adoption and future of AI in the banking industry are not that easy. Existing technology and trends in the banking industry are apparently not enough to accommodate digital transformation in the banking industry. 

Banking digital transformation needs to be implemented effectively, with the help of new technology for banking and a digital transformation guide for the industry to reap all of the benefits. They are faced with certain challenges that we will cover in this article. We will read all about AI in the banking sector now and its impact and application in the same.

What is AI in banking?

AI in the banking industry can be best described by the applications that it has to offer. By lowering fraud and boosting compliance, the application of advanced data analytics powered by artificial intelligence is most likely to alter banking in the future. Thanks to AI algorithms, anti-money laundering activities that once required hours or days can now be finished in a matter of seconds. 

Owing to AI, banks can manage enormous amounts of data at breakneck speeds to extract valuable information from it. A wider customer base may get access to better services with the help of technologies like digital payment assistants, AI bots, and biometric fraud detection systems. This leads to increased sales, decreased costs,  increased profitability, and happier customers.

AI applications improve service quality and assist businesses in identifying and thwarting fraudulent transactions by combining the strength of data, clever ML algorithms, analytics, and secure in-app integrations. Moreover, end-to-end banking and financial process digitization is also accelerated by artificial intelligence in banking. 

Many businesses think AI will revolutionize banking since it can carry out a variety of tasks more quickly, easily, and securely. Tools for voice recognition, predictive analytics, and machine learning are all enhancing the value of online banking services. 

Impact of AI on the banking sector

In the banking industry, artificial intelligence makes banks more effective, reliable, helpful, and perceptive. In this digital age, it is giving modern banks an edge over their competitors. The expanding influence of AI in the banking sector reduces operational costs, enhances customer service, and automates processes. Additionally, AI in banking aids users in choosing loan amounts with competitive interest rates. 

Banking institutions can automatically update operations and adhere to current regulatory compliance thanks to AI technology. Apart from these, let’s exclusively look at the impact of AI in the banking sector: 

1. AI impacts the banking sector by lowering operational costs

The banking industry will undoubtedly have to deal with several issues, including paperwork. Bank employees must manage loads of paperwork every day. Such laborious, repetitive processes may increase operating costs and increase the likelihood of human error. AI can help with this resolution. 

These time-consuming and error-prone human activities are eliminated. For instance, banks may use automation tools, machine learning (ML), and AI assistants to automate many aspects of human work. AI is also crucial for enabling banking institutions to broaden the scope of their current operations, which reduces operational costs and creates new revenue sources.

2. AI impacts the customer service department

The use of artificial intelligence in banking, particularly in the customer care sector, has allowed financial institutions to affect how their clients perceive them. Customer satisfaction has a direct impact on any company's performance and bottom line, and the banking industry is no exception. Owing to AI chatbots and voice assistants, banks can now offer their customers 24/7 service regardless of their location or time zone.

Additionally, by using AI and ML for quicker and more thorough research, banks may effectively address customer needs by gathering important information from their digital footprint and payment behavior. 

Furthermore, AI helps the bank personalize its offerings for a whole different audience, thereby increasing the number of its current customers. Financial institutions can now provide the right services to their consumers at the right moment, thanks to AI.

3. AI improves risk management

The banking sector's approach to risk management has been significantly impacted by AI. Financial organizations like banks are at risk because of the type of data they handle daily. They deploy AI-powered tools that can quickly spot trends from numerous sources and evaluate huge amounts of data.

This aids in predicting and preventing credit risks by identifying individuals or businesses that may not be able to pay their loans back. It can also detect unethical activities like money laundering and identity theft. AI technologies and algorithms have changed risk management, resulting in a more secure and dependable banking experience. Therefore, it is clear that risk management has improved as a result of the usage of artificial intelligence in banking.

How can banks transform their core technology to become AI-first?

Banking digital transformation or simply banking transformation with respect to AI is a crucial process. Banks must invest in their ability to transform in four aspects: AI-powered decision-making, operating model, customer engagement, and core technology. 

While each of them has a distinct significant part to play, when they all operate together, a bank can provide consumers with unique multidimensional experiences and drive quick innovation cycles, both of which are essential for staying ahead of the competition.

artificial intelligence banking

1. AI-powered decision-making

To be able to offer personalized messages and choices to millions of users in close to real-time, banks will need to make the most of AI technology. The application of AI techniques across several bank domains could either replace or supplement human judgment to create meaningful results (such as improved accuracy), improve customer experience, and provide useful insights for staff.

2. Platform operating model

Banks will require a new operating model for their businesses to attain the necessary flexibility and unleash value across the major domains of the banking sector to integrate AI successfully. 

The platform operating model imagines a bank with cross-functional teams set up as several platforms. Every platform team is in charge of its resources, including infrastructure, data, and KPIs. In exchange, the group provides a range of goods or services to clients or other platforms inside the bank.

3. Customer engagement

Due to the quick development of technology, customers now expect their banks to be there for them at every step of the way to create a frictionless experience. The bank will need to reinvent how they interact with their customers and make the necessary modifications to be constantly present in their lives and meet their latent and developing requirements.

4. Core technology

Banks need scalable and robust core technology elements, such as a technology-forward strategy, contemporary API architecture, and data management methodologies, to effectively incorporate AI in banking processes. The effectiveness of investments made in the two areas mentioned above - customer engagement and AI-powered decision-making - can be impacted by a weak legacy infrastructure that has to be modernized as well. We will read about this in a subsequent section.

Applications of AI in the banking sector

digital transformation in banking

1. AI chatbots

The use of chatbots in the financial sector is one of the key ways AI is being applied in the banking industry. AI chatbots in banking are modernizing how businesses provide services to their customers. AI chatbots in the banking industry may assist customers at all times and give them in-depth responses to their inquiries. These chatbots offer users an experience that is customized. As a result, banks can boost brand recognition, enhance customer service, and generate revenue by utilizing AI chatbots for banking and finance procedures.

2. Data gathering and analysis

Automated data collecting and analysis is one of the numerous advantages of AI in banking and finance. Processes for data collecting and analysis can be carried out effectively by artificial intelligence in the banking industry. Massive data collections are processed by AI machines, which also uncover insightful information. Banks will be able to foresee business and industry trends easily with the use of this analysis. 

Additionally, the analysis of client data through mobile banking apps equipped with artificial intelligence will be crucial in providing customized services and improving the overall user experience. In addition, banks can use the insights gained from client data to make smart business decisions and provide them with more specialized service offerings.

3. Improves customer experience through AI

AI financial apps are quite useful. The goal of AI-powered mobile banking apps for Android and iOS is to enhance client satisfaction and service level. Utilizing AI and machine learning in banking enables businesses to track user activity and provide more customized services to clients. 

Depending on user search trends, intelligent mobile apps may monitor user behavior and extract insightful information. These data would aid service providers in making tailored suggestions to customers. 

4. Automates and streamlines the process

In the financial industry, AI has a tonne of potential. Artificial intelligence in banking will hasten automation and simplify financial procedures. Automation is one of the best uses of AI in the banking and finance industries. Using AI software, banks can streamline and automate every task presently done by people, making the entire process effective and paperless. 

AI technologies can thereby reduce bankers' workloads and raise the standard of their work. Through customized AI banking apps and AI Chatbot services, users may submit service requests at any time and get prompt responses from virtual banking assistants powered by artificial intelligence.

Challenges in AI adoption in the banking sector

trends in banking

There are two different issues the banking industry must overcome in order to execute flawless banking digital transformation of similar digital transformation in the banking industry. They must continue maintaining the privacy requirements and regulatory compliances while, on the one hand striving for rapidity and flexibility in their operations.

Let's examine the main barriers banks encounter pertaining to digital banking transformation and AI adoption to gain a better understanding of the difficulties.

  • Legacy infrastructure

Artificial intelligence (AI) integration into business processes frequently results in new needs for the infrastructure, data, and technology required to develop and scale models. Large legacy systems are expensive to replace, and developing AI banking applications can be computationally demanding, resulting in prohibitive up-front expenses.

  • The talent gap in data

A group of data experts, comprising data scientists, analysts, engineers, and machine learning scientists, is required for the full-scale deployment of AI initiatives. As a result, the implementation of AI and machine learning across industries, including the financial services sector, continues to be hampered by a dearth of data talent. According to a Deloitte poll, 23% of the most experienced AI users cited a serious shortage of talent skilled in implementing AI. A targeted approach for reskilling and upskilling for particular skills is needed to close this skill gap.

  • Governance arrangements and rules

Any new technology implementation may conflict with the heavily regulated financial services and banking industries. Teams responsible for risk and compliance may find it challenging to identify possible risks or develop pertinent internal rules. Without the proper governance, the implemented AI solution may have unforeseen effects, such as denying access to financial products or violating privacy.

  • No clearly defined strategy

Executives may not fully embrace AI technology in a continually changing technical environment with significant costs for additional human capital and infrastructure. In that case, it will be challenging to successfully adopt an organizational-wide plan to make the most of AI's technological potential.

  • Lack of quality data to run trials on

The adoption of state data privacy regulations in the US and the EU shows that regulators are increasingly examining how businesses are handling data. Furthermore, due to regional variations in data privacy legislation, cross-border data sharing is still somewhat restricted. Due to these regulations, there is a dearth of data on which machine learning models may be trained and predictions made.

Nevertheless, banks can meet the AI challenge is something that only time can tell!

What may the future AI Bank look like?

The AI-first bank, after undergoing several banking transformations, will hopefully offer experiences and propositions that have the following qualities in order to meet customers' increasing expectations and knock competitive challenges in the AI-powered information age -

  • Intelligent - That is, suggesting actions, predicting, and streamlining key decisions or tasks
  • Truly provide a uniform customer experience - That is seamlessly encompassing the physical as well as online contexts across multichannel platforms 
  • Personalized - That is, timely and reliable, and based on detailed knowledge and understanding of the customer's past behavior and circumstances

Conclusion

Transforming capabilities or the occurrence of banking digital transformation at all levels of the capability stack is a necessary step on the path to becoming an AI-first bank. A sub-optimal space that cannot achieve banking organizational goals will arise from ignoring problems or underinvesting in any one aspect of transformation. 

Making sure AI technologies are used throughout the organization is no longer a choice for many banks; it is now a strategic must. Success will depend on how the bank is envisioned through digital banking transformation and how its capabilities are developed comprehensively across the different layers and aspects of banking.

However, the extent of the presence of AI in the banking sector is also something that needs to be considered in order to evaluate the presence of digital transformation in banking in general and digital transformation in finance in particular.

Aparna <span>Growth Strategist</span>
Written By
Aparna Growth Strategist

Aparna is a growth specialist with handsful knowledge in business development. She values marketing as key a driver for sales, keeping up with the latest in the Mobile App industry. Her getting things done attitude makes her a magnet for the trickiest of tasks. In free times, which are few and far between, you can catch up with her at a game of Fussball.

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