Small banks and credit unions play an important role in the US financial system particularly in serving rural and small metropolitan areas.
Their essence is in relationships built with the community at the local level, which cannot be established by large banks without a deep regional presence. The key product offerings of small banks are loans to small businesses, mortgages and farm loans.
They are also the primary data collectors about local customer banking needs; this is possible because of their interactions at a customer-banker level. Their underwriting is also more flexible to suit the individual requirements of a business as compared to larger institutions that have a one size fit all approach for scalability purpose.
But despite the higher satisfaction level of customers as shown in the graph above, small banks seem to be the worst affected by the housing meltdown. The irony is that the 2008-09 financial crisis originated from large Wall Street Banks. But the small banks and credit unions were not immune to the recession that followed and took a severe financial beating during the same. Unfortunately, the setbacks have led to a massive consolidation with large banks seeing an ever-increasing share of both deposits and loans.
Challenges Faced by small banks and credit unions:
- Increased competition from fintech platforms – facing a multi-pronged competition from fintechs in the field of payments, wealth management and especially online lending.
- Regulatory Compliance– The regulatory burden has increased manifold after the financial crisis that has led to an escalation in costs for small banks. These costs have led to margin erosion and are ultimately being passed to the final c
- Technological Advancement– Small Banks legacy IT systems are not conducive to host latest online technologies. The advent of smartphones has raised customer expectations from banking services. However, often small banks are unable to invest in their IT platforms and not able to deliver a true digital experience.
- Complex Procedures– Repetitive visits to the bank branch and involvement of excessive documentation during the borrowing process makes it highly inefficient. Compare that with marketplace lenders who are offering loans within 2-5 working days and from the comfort of applicant’s own home.
- Cybersecurity concerns – Small Banks’ security systems are not sophisticated and are prone to security breaches. In an era where digital privacy is at the top of the mind of every single customer and regulator, small banks are on thin ice with their data security practices.
- Dealing with Internal Issues – Small banks are hampered, as they need to concentrate on compliance and internal issues; the focus on improving customer experience has taken a back seat.
Introduction of Artificial Intelligence and Machine Learning in Small Banks and Credit Unions:
Artificial Intelligence (AI) and Machine Learning (ML) will usher a new era in the banking world.
The value that can be derived by incorporating AI and ML in their business ecosystems is enormous and will be a game changer for many small banks.
Let us analyze the key facts in the evolution of AI and ML and how they will affect the banking industry.
A). Cheap computing power
The growth of cloud and allied sectors has led to a revolution. Computing power is now cheap, flexible and even the smallest of banks can now compete with Wall Street Banks by hosting their servers with players like AWS.
Now the small banks are not forced to invest heavily upfront on servers, data farms and applications and can adopt a pay as you go model to keep capital spending on IT in check.
This allows Small Banks and Credit Unions to leverage their existing data to build intelligent applications customized to their clients’ specific needs.
B) Efficiency in Operations
Regulatory needs require a bank to generate millions of documents on a yearly basis. Auditing these documents and then filing them for compliance is a major headache. Robotic Process Automation (RPA) is a revolutionary solution to the problem of documentation. All the backend operations can be addressed by the AI powered RPA cost effectively. RPA is already a huge success in the banking sector and small banks need to get on the bandwagon to ensure efficiency.
C) Large scale data availability
Artificial intelligence system requires data in massive troves in order to learn. Banking Sector is a hub of a considerably large amount of information. Banks can leverage the proprietary customer data to access insights that can help them in serving their customers better. This can start from the CRM experience to better underwriting to cross selling of products and services.
D) Fraud detection and anti-money laundering
Fraudsters are increasingly becoming more sophisticated and are devising new technologies to dupe banks and its customers. But it is also important that the banks do not hamper the overall customer experience to enhance security.
Machine Learning is the perfect answer to the problems mentioned above.
It will not only take the existing data but will leverage real time operational data to improve its algorithms. So the security system to catch frauds and launderers is improving on a suo moto basis. The efficiency of ML grows as more data is fed to it, so over time it will increase in sophistication and its ability to stop illegal activities will self-enhance. So it becomes imperative that banks invest in ML as early as possible to take advantage of the scale of their own data and its subsequent learnings.
E) Underwriting and Investment Analytics
Credit algorithms are being increasingly powered by technology. Fintechs have replaced the role of the traditional credit officer and have focused on leveraging AI and ML for their credit decisioning process.
The underwriting process in almost majority of the marketplace lenders is now overseen by AI and ML powered credit models with little to no intervention from humans.
Similar Wall Street is witnessing the takeover of the investment sector by Quant funds that are investing billions of dollars in AI and ML technology to gain edge over other investing peers.
Source: https://outline.com/JqbuHp WSJ.
The above graph showcases how AI and ML can help Small Banks and Credit Unions realize their strength of understanding their customers deeply and apply it on scale. Small banks are hemorrhaging money in credit card losses and AI powered credit algorithms can assist their underwriting department in making smarter credit portfolio bets.
Upgrading the Banking System
With the Fintechs taking big strides in capturing the banking space, small banks are facing an existential threat. Integration of the best-in-class technologies: Artificial Intelligence and machine learning has helped fintech lenders create a massive impact. As banks look to incorporate the latest technology, their CIOs will see that every single solution being offered is in some way powered by AI and ML.
Just investing in AI and ML does not put an end to the challenges being faced by small banks.Their needs to be a change in the mindset and AI and ML should become part of the Banks’ core technology.
A partnership approach will best serve small banks in taking the correct step in the AI and ML world.
MonJa – Small Business Loan Underwriting Platform
MonJa offers a “3-in-1” SMB Loan Underwriting Platform. It is an end-to-end solution for banks and credit unions that are lending to small businesses.
MonJa’s Underwriting Platform includes Automated Underwriting, Loan Scoring and Proprietary AI-Powered Algorithm.
This software package enables banks and credit unions to underwrite small business loans efficiently and effectively, allowing institutions to attract and retain the best business clients. With the MonJa underwriting platform, a bank can serve a range of loan sizes, including smaller loans that would otherwise be unprofitable to underwrite. MonJa’s suite of underwriting modules is perfect for banks and credit unions that want to expand small business lending volume without high upfront costs and while improving experience and efficiency of lending to business customers.
- Easy deployment leveraging our Software-as-a-Service for rapid integration and low startup costs
- Objective and independent underwriting using MonJa Loan Score Model
- Increased profit: open new business loan underwriting opportunities particularly for loan sizes under $100,000 that woud otherwise be uneconomical
- Time savings: MonJa’s advance AI reviews loan applications in seconds rather than days
- Security – 2048-bit TLS end-to-end transport encryption ensures data security
- Modern customer experience: easy, intuitive loan application process for mobile devices
- Client rentention – faster approval and funding process increases client satisfaction and retention
- Dedicated account representative – MonJa stands behind your implementation
- Flexible fee model- pay-as-you-go model that scales with your origination volume
Powering SMB underwriting with MonJa allows financial institutions to fasten the process while simultaneously reducing the expenditure on each individual case. Additionally, MonJa provides Banks and Credit Unions with extra layer of analytics which will provide deep insight into their lending practices.
Sounds interesting? Read more here: MonJa’s AI-powered Loan Underwriting Platform.
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Our platform supports C&I, CRE, auto and other small business loans.