Looking at the way Artificial Intelligence (AI) is heading, it goes without saying that Artificial intelligence is transitioning from an advanced option to a core capability in not just personal banking but the banking sector as a whole. However, making effective use of ever-increasing data pools necessitates understanding the technology and the related trends.
First, let us look into the impact of AI in banking and then the emerging trends that are spearheading banking in a whole new direction. We could say these are the hottest banking technologies, which have never cracked the list of 2020 and 2021. However, as trend reports show, they are strong contenders in 2022.
Let’s get down to it.
AI in Fintech
Because AI can improve processes in virtually every aspect of banking, the potential benefits are enormous. AI is providing significant benefits to banks implementing it, from back-office automation to customer-facing operations. With new features and solutions steadily pouring out, AI in banking has already seen impressive adoption rates.
Effective Cash Flow Forecasting
A cash flow forecast is an essential part of financial decision-making. Manual adjustments can obscure the valuable insights provided by the data and can significantly impact the forecast’s realistic or successful usability. Many banks who have experimented with AI say that it can make forecasting feel easy for users while also providing reliable results. When a company implements AI, it consistently learns the cash flow patterns and provides an accurate forecast unique to that company.
Risk management
Although credit is an important function of banks, granting loans has always been regarded as a risk. Banks typically use extensive historical credit data to assess an applicant’s lending risk. AI-assisted underwriting provides a more comprehensive view of an applicant, allowing banks to make more informed decisions when sanctioning loans.
It combines big and traditional data, as well as social, business, and internet data and unstructured data. To improve the accuracy of credit card fraud detection, AI and analytics-aided techniques can detect anomalous behavior, provide multivariate forecasting, and improve risk control.
Fraud detection
Machine learning (ML) fraud detection is one of the most critical applications of AI in banking.
As technology has made banking more accessible, it has also increased the opportunity for fraud to take advantage of these services. Machine learning, which connects data points faster and more accurately than humans, can be used to detect fraud. Because these systems improve with larger data sets, the longer banks use them, the more accurate they become. Experts believe that fraud might become less successful as more institutions implement AI fraud detection.
Regulatory Compliance
It is important for banks to ensure that new technologies and services meet relevant standards as they are implemented. A typical regulatory compliance program demands the collaboration of multiple lines of business and corporate functions. However, this frequently results in poor quality, mismatch of formats and standards, and confusion.
AI can make sense of these disparate data sources and combine them into actionable insights. Banks can then improve their compliance in the face of ever-increasing regulation.
Banks have begun to identify meaningful AI applications across their front and middle offices. Winning AI strategies are experimented with and deployed. They are becoming the living examples of how banks can best approach an AI-enabled digital transformation.
Now, let’s explore some trends that are transforming modern banking systems.
Trends that are reshaping the Banking Industry
Digital Loan Origination
Digital lending is the way of the future, and the ability to capture and engage borrowers effectively throughout the lending process is essential. However, the movement of banks and credit unions to these platforms is not as strong as many believe.
With deposit levels surging, the industry’s focus has shifted back to lending, and digital loan origination systems have finally found a place on the list.
P2P Payments
Although only a few financial institutions have deployed real-time payments, more banks are expected to follow suit in 2022. P2P payments are the most frequently cited use case for financial institutions’ faster payment programs.
Consumers can now send money from their banking app and let recipients choose where the money should be sent. When it comes to providing instant payment capabilities, financial institutions now have a choice rather than being forced to use an expensive, restrictive, and closed network.
Data Fabric
Banks and credit unions have worked hard to integrate their existing data silos and have had some success. The main problem is that they get stuck when it comes to external data, which may be locked behind firewalls or scattered across the globe. They cannot access data quickly and efficiently enough to support real-time customer self-service and analytics.
Data Fabric helps overcome this problem by providing banks and credit unions with a unified user interface and a holistic view of internal/external data, relieving the muddling task of integrating individual data streams with individual applications.
Conclusion
Businesses in the financial services sector that rely on data-driven methods and AI are changing how we interact with money. It would be hard to say that banks and financial institutions would entirely depend on AI and Machine Learning. But these technologies are the waves of the future, and banks are willing to surf on those waves. Adopting digitization and artificial intelligence (AI) will unleash the power of advanced data analytics to combat anomalies and improve compliance.
