There is no denying the reality that artificial intelligence is setting new standards in the financial sector. In fact, AI is the basis for the sudden boom in Fintech. We have talked extensively about the role of AI in investment management and insurance.
However, there are other segments of the financial industry that also rely on AI technology. The banking industry is among them. Banks have been slower to adapt AI technology than some other institutions. They currently spend just under $4 billion in 2020. However, the market for AI in banking is expected to grow over 30% a year and will be worth over $64 billion by 2030.
New software uses AI to manage bank loans. Banks are finding ever more creative applications to AI technology to conduct actuarial analyses.
AI Makes Bank Lending Software Far More Reliable
The upcoming year is going to be transformational for banks, while the pandemic times sufficiently changed the customer needs related to payments, deposits, and lending. In this article, we decided to cover the tendencies in banking loan software in 2022 and give a brief market outlook of AI-driven lending software as a whole.
Digital banking market
The Deloitte report says that in the second quarter of 2020 the largest 100 banks in the USA reported $103.4 billion in net loan losses due to the pandemic burst. However, it didn’t stop the growth of the lending segment in banking. On the contrary, COVID-related challenges became opportunities and had a major shaping impact on the banking tech forcing incumbents to invest in digital channels. From blockchain adoption to the use of AI scoring and digital underwriting – a lot of banking processes became paperless and online. They have found that AI has truly changed the lending process.
2020 became the year when a lot of customers first experienced their remote interaction with banks and enjoyed it. Growing trust in online and mobile banking due to meeting customers’ resource-saving expectations was the driving force behind digital lending growth in 2020-2021.
This is why banks are working on the quality of their digital lending platforms and expanding their functionality with AI technology. Here is what they include now:
1. End-to-end loan management with AI
According to Accenture, half of the banking routine is still performed manually and can be streamlined. It refers to underwriting, customer onboarding, document management, analysis, and statistics. Using up-to-date lending software, banks solely in the North American market have an opportunity to save over $70 billion by 2025. Automation assists employees and allows them to serve a larger number of borrowers. Decision-making and loan servicing take less time, while customer satisfaction grows. The relationship managers get access to relevant information about borrowers, too.
Online loan applications and third-party services integration for processing them can be game-changers as they use AI to provide a convenient way to apply for a loan and to consider the application using relevant data.
Loan approval, as one of the biggest bottlenecks due to inconsistency of information between teams, may increase business risks. However, the process of decision-making can also be automated by lending business software that has sophisticated AI features. Such an approach speeds up the process and ensures everyone is on the same page.
Due to interactive dashboards available on digital lending platforms, banks can monitor customer interactions, keep track of their risks and financial results, access document databases, and get relevant analytics.
2. Integrated lending module
Many banks are not ready to replace their core banking systems only to improve their lending segment.
A great way out is to use cloud-based microservices software that can be used as a middleware integrated into the existing digital banking solutions. This software typically has very sophisticated AI algorithms that help improve its functionality. Banks can add such services as modules, and remain flexible without extending the budget.
3. Exploring partnerships and business opportunities
Digital lending can exist in a number of ways, like POS (Point of Sale) transaction model or embedded lending, for example, Buy Now, Pay Later. All these business opportunities require top-notch AI technology solutions.
As for partnerships, lenders can collaborate with data providers, 3rd party processors, collectors, and AI technology companies to expand their customer base and automate more routine transactions. A popular direction is partnering with digital compliance providers for online ID verification, KYC/AML, and so on.
4. Automated credit scoring
Conventional scoring takes time and can miss significant factors leading to false-negative and false-positive decisions. In addition, traditional scoring implies extensive documentation, high-interest rates, increased decision-making times. Implementing alternative data-driven scoring methods in addition to conventional ones may improve the processes in digital banking. Alternative data includes personal data, business data (like cash flows, POS transactions, bank account statements, and financial statements), and behavioral data (like spending habits or psychometric tests).
5. Customer-centered banking and omnichannel capabilities
Digital lending allows AI-driven self-service with a high level of support and personalized offerings. Customers can apply from home and receive money on their bank accounts in hours, not weeks. All that is tightly connected with omnichannel interaction, where customers’ preferences are prioritized. Digital lending platforms in banks can be integrated with chatbots, SMS, and email services for notifications and assistance to provide borrowers with the needed balance of in-person and digital communications.
Loan Software in Banking in 2022: The Bottom Line
Dominated by technology, data, and a customer-centered approach, the lending ecosystem in banks is going to embrace the change. Banks still need to deal with compliance and regulations issues, but it’s not going to stop the digitization of the loan lifecycle.
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