One of the most panic questions to business owner is the processing time of business financing generally take longer time than the business financing needs. In traditional commercial banks, the average time to funding of Corporate Lending (a general business financing term including SME financing) is generally around Three Months. The time to funding count from the financing application to funding deposited to bank account. From an experienced banker point of view, these processing cycle make sense as data collection, KYC, industry research, data verification, internal credit proposal parathion, credit approval negotiation and account opening all need time to process. From business owner perspective, these times is unacceptable as business needs change quickly and seldom has business can foresee the financing needs prior three months ago. Base on the pain point, FinMonster propose to reduce the processing time from three months to two weeks by adopting alternative data under Distributed Ledger Technology (“DLT”) given DLT could enhance the data security and privacy. The processing time means the time to funding reach company’s bank account.

Traditional Problem of Lending Process

As mentioned in another article, “Corporate Banking Future”, traditional corporate bank act as an intermediary in business world to facilitate doing business by its policy and procedure which are regulated by local Government Authority. To comply with the regulations, traditional corporate lending process is complicated and highly different from personal financing. Within the lending process, corporate credit assessment is an art which require multi angles to assess a business performance.

In Corporate Bank today, the average time to approval for small business financing is between three and five weeks. A lot of corporate customer do not understand why the processing time take such a long time. The business financing from time to funding start from

1. customer financing request,
2. bank officer (generally relationship manager or credit analyst) collect business information,
3. information verification,
4. financial analysis,
5. business and industry analysis,
6. litigation check,
7. company background check,
8. facility design and account planning,
9. lending rate negotiation,
10. credit proposal preparation per the previous finding,
11. negotiation credit application with credit approver,
12. pass credit committee,
13. prepare credit agreement,
14. signing credit agreement,
15. documentation verification,
16. account opening,
17. KYC process,
18. credit facility limit dispatch in system,
19. receive instruction from client and
20. at last, deposit the financing proceeds.

Bingo! Any single step is delayed from the above 20 steps would cause the funding to account delay or even disapproval. A lot of reader may imagine there is supercomputer to handle all of these. Unfortunately, these processes are mostly handled by the bank staffs manually and input into different systems in the bank for bookkeeping but not for credit decision making.

Digital Transformation of Lending Process

The lengthy process is panic to customer, also to the bank. The required steps hinder the bank to swiftly process the financing request from corporate customer, particular to the financing needs from SME. SME financing is reluctant to banks because the banks have to maximise the return on the time cost from staff on the same processing time while one application from large corporate usually generate more return than a SME, means: cost to income ratio is high. Thus, the leading bank in different regions embrace digital transformation of credit process to reduce the cost to income ratio differentiation between SME and large corporate. Nordic banks have applied the digital transformation for SME by partnering with Fintech in order to grow their SME business. 

Yet, there is no perfect solution to digitalise the whole credit process. Few European Banks or FinTechs are offering compelling solution in SME lending to shorten the processing time for specified financing products. Most of banks propose to automate part of the operations in the credit application but many are dissatisfied due to the legacy IT systems and lack of full stack fintech talent to design and execute the solution for corporate credit application. Corporate lending usually involves Business, Operations, Risk and IT to handle the abovementioned 20 steps. Talent with full knowledge over all these departments is rare or nil. Compare with agile approach of FinTech, there is no single owner for the “Change” from corporate bank to drive the implementation.

Application of Automation and Alternative Data

The end-to-end journey of business financing is able to be reduced to two weeks from customer request by applying automation and Digital Data, both Historical and Alternative Data. Alternative Data has been widely discussed in banking industry for financing. The definition of Alternative Data means the data which has not been used for credit assessment as most of them are unstructured data from third party. By using Alternative Data, credit information could help to forecast the business performance or verify the historical data given that most of the alternative data are up to date and almost real time. The steps from (1) to (7) and (12) to (17) actually can be automated by systems. From FinMonster perspective, we classify steps (1) to (7) as Information Aggregation while steps (12) to (17) as Credit Decision and Funding.

Information Aggregation is one of the frustrated problems from banks. Credit approver has to make sure the information received for credit assessment are genuine and reliable. Unfortunately, most of the information are provided by client for private company business financing. There are no public sources that could verify the financial statement information, the sample invoice detail or the expenditure of a business. The incomplete/unverified business information is the key hurdle to credit approver to sign on the paper for clean lending. To solve the problem, some of the banks verify part of information by cross checking other information platform such as company registry or insurance company. However, part of verified information is not enough to provide confidence on the understanding of a business. Therefore, FinMonster is developing a Loan Application System (“LAS”) allowing company directly apply financing to financial institutions in Asia. FinMonster LAS is designed to aggregate alternative data of a company automatically so that the alternative data from third parties able to verify the historical data provided by customer. For example, the website traffic of a eCommerce company could help to prove the customer engagement and also the popularity of an online shop. The FinMonster LAS will help the banks to automate the step of information collection and verification which could save weeks times and allow relationship manager to focus on relationship building with customer and cross selling to increase the return. More importantly, the new digital journey lower the pressure on relationship manager to hit and run for revenue generation.

Credit Decision and Funding process are hard to be automated provided that banks are regulated. Per McKinsey research, at one bank in central Europe, the long-standing business-lending process features a decision checklist incorporating thousands of criteria and covenants for contracting and disbursement. The long checklist is generally required for complying local regulation but not necessary to the banks. The leading bank in the world is trying to automate the credit decision by data driven solution with the assistance of credit approver who could focus on the transaction that has risk concern. One of the good initiatives of local authority is HKMA from Hong Kong who allow licensed bank in Hong Kong to conduct credit assessment for personal lending by innovative credit analytic tools built upon financial technology. This action definitely provide a high confidence on the development of Financial Technology. FinMonster LAS will integrate the historical data and alternative data from numerous sources and provide credit recommendation base on the big data analysis from third parties. At the beginning, lack of trust in automated decision making is a problem to be faced by banks. Therefore, the system shall be applied to simple banking product to start the transformation and tested by historical transactions. By accumulating the approval data and testing, the accuracy of data-driven credit decision making would improve and tested again. Once the decision is confirmed by credit approver, the documentation, KYC process and fund releasing are easily to be automated with the pre-set automation process.

This is just a start

The challenge to reduce the processing time of business financing is tough. However, as fintech startup, we believe we grow when we face challenge and able to benefit the company who needs funding to support the business and end up benefit the economy which need SME. Digital transformation of corporate lending has been discussed more than decades but the progress was pretty slow due to lack of available data. With the increase trend of SaaS (Software as a Service) application, now is the time to revolute the business financing process and make SME financing as easy as personal financing. By the end of 2020, there will be a new technology driven business financing application to be released.

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