HomeTechnologyThe Synergy of AI and SaaS in Transforming Banking & Payment Solutions

The Synergy of AI and SaaS in Transforming Banking & Payment Solutions

Interest in artificial intelligence technology is sky-high in the banking and finance sector.

The reason is not surprising. The strategic application of AI’s many technologies — including machine learning, natural language processing, and computer vision — can drive meaningful results for banks, from enhancing employee and customer experiences to improving back-office operations.

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The touchpoints do exist, but they are superseded by digital touchpoints. 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 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. 

What are the top benefits of AI in banking?

The following is a list of the top benefits of artificial intelligence (AI) in banking and finance today.

1. Reduction in operational costs and risk

The banking business is mostly digital, yet it is littered with paper-intensive human-based operations. Due to the possibility of human mistakes, banks confront major operational cost and risk challenges in these procedures.

Robotic process automation (RPA), software that simulates human-performed rules-based digital processes, is used in banking to replace much of the time-consuming and error-prone labor involved in inputting client data from contracts, forms, and other sources.

2. Improved customer experience

There’s a reason banking hours were mocked. Banks never appeared to be open when you needed them the most, such as late in the day or on weekends and holidays. Call centers used to be renowned for excessive wait times, and when operators were eventually engaged, they frequently couldn’t handle the customer’s issue.

Chatbots are on the line.  The usage of conversational assistants or chatbots is one of the major advantages of AI in banking. A chatbot, unlike an employee, is available 24 hours a day, seven days a week, and clients have grown increasingly comfortable utilizing this software program to answer inquiries and do many typical banking procedures that traditionally require face-to-face interaction.

Upselling. In addition to handling customer support questions and talks about specific transactions, banks are improving their use of chatbots.

3. Improved fraud detection and regulatory compliance

Banking is one of the most heavily regulated industries in the world, both in the United States and globally. Governments utilize their regulatory authorities to ensure that banks have appropriate risk profiles to avoid large-scale defaults and that banking clients are not utilizing banks to commit financial crimes. 

As a result, banks must adhere to a slew of rules requiring them to know their clients, protect their privacy, monitor wire transactions, prevent money laundering and other forms of fraud, and so on.

4. Improved loan and credit decisions

Similarly, banks are employing AI-based tools to assist them in making better-educated, safer, and lucrative lending and credit choices. Many banks still rely on credit ratings, credit history, customer references, and financial transactions to evaluate whether or not an individual or firm is creditworthy.

However, as many can attest, these credit reporting systems are far from flawless and are frequently plagued with inaccuracies, missing real-world transaction histories, and creditors being misclassified. 

AI-based loan decision systems and machine learning algorithms can look at behaviors and patterns to determine if a customer with a limited credit history might make a good credit customer or find customers whose practices might increase the likelihood of default.

5. Automation of the investment process

Some banks are digging deeper into AI by utilizing their intelligent systems to assist in investment decision-making and support investment banking research. 

AI technologies are being used by firms such as Switzerland’s UBS and the Netherlands’ ING to explore the markets for undiscovered investment possibilities and influence their algorithmic trading systems. While humans continue to be involved in all of these investment choices, AI systems are identifying new opportunities through improved modeling and discovery.

Furthermore, several financial services firms are providing robo-advisers to assist their consumers with portfolio management. These robo-advisers can deliver high-quality financial advice and be available whenever the user wants it through personalization, chatbots, and customer-specific models.

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 you 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 be difficult. 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.




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