The involvement and methods in the creation of financial software had improved dramatically in recent years, particularly when the world economy began to recover from the Covid-induced state-wide lockdowns. Financial services organizations have changed their focus from interacting with clients face-to-face to making their services totally digital and accessible around the clock in this scenario.
To be competitive and accomplish targeted corporate growth in the current era of digitization, firms must keep up with technological developments. The financial applications or services sector is transforming due to the usage of artificial intelligence (AI) and machine learning (ML), which has tremendous advantages for both consumers and FinTech organizations, including more effective business operations, improved financial analysis, and increased client engagement.
A survey on AI adoption by the Economist Intelligence Unit found that 54% of financial services companies with 5,000+ workers have done so. According to the study, 86% of financial services executives intend to increase their investments in AI until 2025.
In this blog, we will aim at understanding the role of AI and ML technologies in redefining the finance landscape transforming everything from banking applications to operations.
Let’s begin!
How are AI (Artificial Intelligence) and ML (Machine Learning) improving the standards of financial apps?
- Apps getting more scalable
The management of enormous sets of micro-segments is possible with AI/ML. By dismantling the massive client clusters produced by conventional macro-segmentation methods, artificial intelligence-driven micro-segmentation enables businesses to engage with customers in more individualized, personalized, and tailored ways. Better targeting and conversion rates result from micro-segmentation.
- Increases client involvement
Product recommendation engines are an advanced form of AI that make suggestions for each user based on their prior behavior, current session activity, product economics, and the preferences and behaviors of other users who behave similarly. Customer engagement can be increased by using AI to understand the customer better and by utilizing real-time decision-making and predictive analysis. For instance, product suggestion engines have successfully provided a personalized experience and increased sales.
- Personalized Reminders
Mobile banking apps integrated with AI allow customers to set various types of individualized reminders for particular transactions. It could be related to the payment of a specific bill, a balance falling below a specified level, or any suspicious transactions. Additionally, personalized reminders about the available bank balance also help the customer keep a tab on spending and regulate it as and when required. By performing these tasks, mobile banking apps ensure that the customer enjoys a superior user experience and safety from fraudulent transactions.
- More Personalization through applications
It is only the first step towards personalized quality banking systems since before providing clients with individualized services, banks must understand how they want to be addressed. Here, there is a tone of work huge be done. Banks need a tone of data that is dispersed over numerous systems and divisions. Artificial intelligence and Machine learning must be used to bring all of this together so that it can mine the data and provide customers with pertinent insights or suggestions.
- Secure Transactions
Millions of data points that are often overlooked by people are analyzed by machine learning algorithms, which are pretty good at spotting transactional fraud. Additionally, ML lowers the number of erroneous rejections and aids in enhancing the accuracy of real-time approvals. These models are typically created based on past transactions and the internet browsing habits of the client.
In addition to detecting fraudulent activity with high accuracy, ML-powered technology is able to recognize suspicious account activity and prevent fraud in real time rather than catching it after the fact.
According to a study, financial institutions pay close to $2.92 in recovery costs for every $1 they lose to fraud. Credit card fraud detection is one of ML’s most effective uses. Typically, banks have monitoring systems that have been programmed using payment history information. Numerous credit card transaction datasets are used for algorithm training, validation, and backtesting. To stop fraudulent transactions, ML-powered classification algorithms can quickly categorize occurrences as fraudulent vs. legitimate.
- Wealth and Portfolio Management
Based on their income and spending patterns, AI-based systems can even identify potential investors. It can also determine market trends and select worthwhile funds depending on their portfolio. The most remarkable aspect of this is that it can be carried out virtually without visiting the bank branch.
Investment funds? Fixed-rate deposits? You may accomplish all of this and more from the convenience of your home. All thanks to AI and ML
- Chatbot
Chatbots deliver a very high ROI in cost savings, making them one of the most commonly used applications of AI across industries. Chatbots can effectively tackle most commonly accessed tasks, such as balance inquiry, accessing mini statements, fund transfers, etc. This helps reduce the load from other channels such as contact centers, internet banking, etc.
- Systems for Integrated Command and Control
Data engineering and machine learning methods can be used to combine the many data sources to construct these single sources of truth systems. Integrated Command & Control Center is the name given to this. The advantage of this strategy is that it enables banks to make better judgments based on all of the information at their disposal as opposed to just a subset of it.
Wrapping up
Even while AI and machine learning are still in their initial stage in the financial industry, we can only hope that they will be used even more widely. We can’t overlook the numerous applications of AI and ML in the finance sector.
Fortunately, financial institutions are starting to comprehend the type of influence that modern technologies like AI and ML have. The reality is that most banks adhere to rigid procedures, which make it difficult for them to become technologically advanced organizations in terms of operations and organizational structure. Thus, it is high time for Banks to have faith in these technologies and get ready to implement them in order to overcome the fiercely competitive landscape. More importantly, harnessing AI and ML capabilities can be the ultimate game changer in terms of offering a reliable, secure, and personalized experience to users.
Author Bio: Kanika Vatsyayan is Vice-President Delivery and Operations at BugRaptors who oversees all the quality control and assurance strategies for client engagements. She loves to share her knowledge with others through blogging. Being a voracious blogger, she published countless informative blogs to educate audience about automation and manual testing.
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