The banking and financial industry is very interested in artificial intelligence technology.
The reason is not surprising. Natural language processing, computer vision, and machine learning are just a handful of the many AI technologies that can be strategically used to enhance back-office operations and produce noteworthy results for banks.
According to McKinsey, AI technologies have the potential to generate up to $1 trillion additional value each year.
During the last decade, brick-and-mortar branches and ATMs were the contact centers. These were the avenues or the channels to interact with the customers.
How does banking look today?
The touchpoints do exist, but digital touchpoints supersede them. During the last few years, there have been dramatic changes in customer behavior and preference patterns.
The bulk of transactions today go through digital channels. Smartphones are becoming an important device that is revolutionizing the way we live in today’s world.
Gen-Z is entering the consumer landscape, and the preferred way of banking is changing. So, apps and payment gateways are becoming popular in financial transactions, creating a demand in the fintech app development process.
Hence, we can decode a complete mindset shift in the consumers, and to cater to it, banking also needs to adapt. That is where newer technologies, such as AI and ML, are adopted.
Evolving Customer Expectations
As mentioned earlier, there’s a consumer mindset shift, but consumers now expect more personalized and timely solutions. Financial institutions have to change the pattern in which they approach their customers.
What is more important in banking? There are many things important, but at the heart of all is the customer experience. It becomes the real winning strategy if you get your customer experience right in a competitive marketplace.
Many of us must have received calls regularly from banks or financial institutions, where they call for some offers. Most of the time, we simply put it down and ignore it.
However, there are some instances when we keep the phone down and we feel wow, they know our preferences. The reason why the customer’s ‘wow’ happened is because the communication was personally relevenat and timely.
For that to happen, it is equally important to know the customers well. Not only do they want personalized treatment, but they also to be heard. They want quicker turnarounds, seamlessly manage their financial needs, and want to make out the most from their mobile and digital journey.
To cater to all these demands, things done traditionally do not fit, and therefore solutions should be unique, and that is where AI and ML are making a big difference.
The Data revolution that makes it the new oil
Data is growing exponentially, and to tackle it, there are different algorithms required data is also supplemented today by a lot of unstructured data.
If we think about it, there are legacy systems, but now we are getting much more digital data such as chat data, call data, audio data, surveys, feedback, and images.
To compute it, better cloud native development strategies are becoming more necessary. Open-source tools like Python and Tenser flow are used as they can handle a lot of algorithms.
In different aspects of banking other use cases are used. Analytics in banking have traditionally revolved around prediction. Most of them can be easily moved to the MLA framework. Digital prospect targeting, real-time screening of fraud risk, and customer credit profile differentiation are various techniques used at the acquisition stage.
On the other hand customer management can be managed by recommender engines and data mining of unstructured data, chat transcripts, natural language processing, and deep learning to get detailed insights.
Conversational banking is becoming important, particularly in customer service. Chatbots are being adopted as AI solutions in the market today.
Importance of AI in banking
The top advantages of artificial intelligence (AI) in banking and finance are listed below.
1. A decrease in risk and operating expenses
Although the banking industry is primarily digital, there are still many paper-intensive, human-based processes within it. Banks have significant operational costs and risk issues in these procedures since human error is a possibility.
Banking uses robotic process automation (RPA) to replace a large portion of labor-intensive and labor-intensive labor-intensive customer data entry from contracts, forms, and other sources. RPA is software that mimics human-performed rules-based digital operations.
2. Smooth experience for customers
The banking hours were made fun of for a reason. When you needed banks most, like late at night or on weekends and holidays, they never seemed to be open. In the past, call centers were notorious for having long wait times, and even when an operator did answer, they frequently weren’t able to resolve the customer’s problem.
The chatbots are waiting. One of the main benefits of AI in banking is the use of chatbots or conversational assistants. Unlike an employee, a chatbot is accessible around the clock, every day of the week. Customers are becoming more at ease using this software to handle many routine banking tasks that usually call for in-person communication, such as answering questions.
3. Enhanced compliance with regulations and fraud detection
Among the sectors with the highest levels of regulation worldwide and in the US is banking. Governments use their regulatory powers to make sure that banks are not being used by banking customers to commit financial crimes and that they have the right risk profiles to prevent large-scale defaults.
Because of this, banks have to follow a long list of regulations that include getting to know their customers, safeguarding their privacy, keeping an eye on wire transfers, stopping money laundering and other types of fraud, and much more.
4. Better judgments regarding loans and credit
In a similar vein, banks are using AI-powered technologies to help them make more informed, profitable, and safe lending and credit decisions. To determine if a person or business is creditworthy, many banks still rely on credit ratings, credit histories, customer references, and financial transactions.
But as many can confirm, these credit reporting systems are far from perfect; errors, missing real-world transaction records, and incorrect credit classifications are commonplace.
Artificial intelligence (AI)-driven loan decision systems and machine learning algorithms can identify consumers whose actions may raise the risk of default or analyze patterns and behaviors to assess whether a client with a short credit history would be a good credit candidate.
5. Investment process automation
By using their intelligent systems to enhance investment banking research and aid in investment decision-making, several banks are delving deeper into AI.
Businesses like the Netherlands’ ING and Switzerland’s UBS are utilizing AI technologies to influence their algorithmic trading systems and scour the markets for untapped investment opportunities. All of these investment decisions are still made by people, but AI systems are finding new opportunities thanks to better modeling and discovery.
In addition, several financial services companies offer robo-advisers to help their clients manage their portfolios. Through personalization, chatbots, and customer-specific models, these robo-advisers may provide high-quality financial advice and be accessible anytime the user needs it.
How one-stop payment solutions benefit SaaS business owners
The development and management of a SaaS platform necessitates a complex network of moving elements and specialized team members. As a result, payment processing is best entrusted to a single convenient payment solution. As a result, SaaS platform owners may focus their efforts on other activities while remaining confident that their payments are being processed smoothly.
Every minute spent on manual payment processing is a minute taken away from expanding your business and supporting your consumers in today’s modern business world. As a result, utilizing payment automation services provided by payment gateways and payment service providers is the natural solution to such difficulties.
Payment automation saves time, reduces errors, improves cash flow management, and strengthens supplier and customer relationships. Companies can use payment automation to streamline their payment workflows by automating manual tasks such as data entry, and invoice processing, and payment features such as recurring payments, split payments, and payout disbursement.
Businesses can eliminate time-consuming paper-based processes that are error-prone, slow, and environmentally unfriendly by implementing payment automation. Instead, organizations such as SaaS platforms can handle their payment demands by leveraging technology such as artificial intelligence (AI) and machine learning.
Better yet, optimized and automated payment flows combined with machine learning can yield critical data insights that can inform long-term company choices. These types of insights are frequently difficult and time-consuming to obtain if obtained manually.
Final Thoughts
Despite the exciting possibilities that AI technology offers for enhancing the consumer experience in banking, incorporating it into financial products may take time and effort. One of the most difficult tasks is ensuring the security and privacy of client data. Banks should guarantee that their chat interface is safe and that sensitive data is not accessed or disclosed by unauthorized parties.
Another problem is teaching an AI model the vocabulary and terminology of the financial business. Banks should supply relevant training data and connect the model with their current systems to guarantee that they can respond to customer inquiries accurately and appropriately.
Banks that invest in software product development services can gain a competitive advantage by adopting the latest AI technologies to improve their operations and services.
