In today’s current corporate lending solutions, manual workflows, paper-based documentation and silo-based systems make the entire lending process complex and time-consuming. The declining interest rates have put pressure on financial institutions to automate their lending processes. More banks are now looking to incorporate an effective system that ensures faster processes, better operational efficiency and full capitalization on the lending opportunities available.
If banks do not transform their lending system now, they risk losing their business to other efficient banks and shadow lenders such as institutional investors, P2P lenders and business development companies.
It's time for banks to transform their corporate lending solution to analyze borrowers' global risk faster and more efficiently; adhere to changing regulatory norms and meet financial needs of all types of organizations from small companies to large multinationals with operations across the globe.
Founded in 2017, Fintep plunges into this industry to build and deploy an innovative, next-generation technology software and cloud ecosystem. Our SaaS business model means that we can serve customers effectively, regardless of their size or location; from global financial institutions to community banks and credit unions.
Fintep brings deep expertise and an unrivaled range of pre-integrated solutions from transaction banking to lending, and capital markets. With a global footprint and the broadest set of financial software solutions available on the market, Fintep manages more than 800 Billion EUR loans over 32 different jurisdictions including the top 100 banks globally.
The concept of federated learning was first proposed by Google in 2017. Google’s main idea is to build machine-learning models based on datasets that are distributed across multiple devices while preventing data leakage and visibility. This type of machine learning does not lack of its own problems, such as communication cost in massive distribution, unbalanced data distribution, and device reliability. These factors must be taken into consideration when deciding whether to dive into this type of technologies, if only for the optimization difficulties. In addition, data is partitioned by user, entities or device Ids, therefore, horizontally in the data space.
But when it comes to data privacy-preserving machine learning approximations in a decentralized collaborative-learning setting, the advantages start to overcome the difficulties. Depending on the parties and data distribution involved, the privacy necessities or even the encryption adopted, multiple architectural approaches have been developed (Differential Privacy, Secure Multiparty Computation, Homomorphic Encryption, ...). But overall, there are two main architectural implementations of federated machine learning.
1. Horizontal Federated Learning
Horizontal federated learning, or sample-based federated learning, is introduced in the scenarios in which datasets share the same feature space but different space in samples.
For example, two regional banks may have very different user groups from their respective regions, and the intersection set of their users is very small. However, their business is very similar, so the feature spaces are the same. A collaboratively deep-learning scheme in which participants train independently and share only subsets of updates of parameters could make sense in this situation.
2. Vertical Federated Learning
Privacy-preserving machine-learning algorithms have been proposed for vertically partitioned data, including cooperative statistical analysis, association rule mining, secure linear regression, classification, and gradient descent.
For instance, a vertical federated-learning approach would be fitting to train a privacy-preserving logistic regression model. The effect of entity resolution on learning performance and applied Taylor approximation to the loss and gradient functions so that homomorphic encryption can be adopted for privacy-preserving computations. Vertical federated learning or feature-based federated learning is applicable to the cases in which two datasets share the same sample ID space but differ in feature space.
For example, consider two different companies in the same city: one is a bank and the other is an ecommerce company. Their user sets are likely to contain most of the residents of the area; thus, the intersection of their user space is large. However, since the bank records the user’s revenue and expenditure behavior and credit rating and the e-commerce retains the user’s browsing and purchasing history, their feature spaces are very different. Suppose that we want both parties to have a prediction model for product purchases based on user and product information.
When diving into the world of machine learning, one of the most challenging tasks for an AI company is getting access to large amounts of relevant data, specially in a world as sensitive and private as the banking industry. Banks have long ago understood the value of their data, but they struggle when considering the best way of monetizing it while preserving user´s confidential data. Furthermore, the quality of any machine learning model and AI is limited by the amount and type of data that is trained with, which limits the quality of the predictive models to only the data that each of the entities holds within its systems.
This is why in Fintep, we have adopted a new way of training, maintaining and improving upon our AI: Federated Machine Learning. This approach allows each of the participants in the federated system to contribute and benefit from the training of an AI with the data never leaving their own datacenters, and also minimizing the number of transactions with the outside world, thus reducing possible security threats.
Another advantage that this technique brings into the table is the training speed and market adaptability. Let´s take for instance a problem that a lot of our clients are struggling with right now: LIBOR transition. Given the fact that this has been a relatively recent need in the banking sector, there was no model to deal with this eventuality. Traditional machine learning systems would have required dataset preparation, model training and validation by each of the users separately, which would have resulted in a costly, lengthy task, and with low to medium accuracy depending on the dataset size of each of the entities. But with Federated machine learning, most of this work can be shared without compromising the data, thus extremely accelerating the time to market of functional models as well as increasing significantly the accuracy of the extraction engine.
In this unprecedented time, corporate borrowers are concerned about access to liquidity as well as compliance with payment and other obligations under their credit documents.
We have recently seen various government regulators focused on ensuring that financial institutions work with issuers. For example, the Executive Order 202.9, issued by New York State Governor Andrew M. Cuomo on Saturday, March 21, 2020, declares it an “unsafe and unsound business practice” if a bank does not grant forbearance to a business that has a financial hardship as a result of the COVID-19 pandemic. The Interagency Statement on Loan Modifications and Reporting for Financial Institutions Working with Customers Affected by the Coronavirus, issued by the board of governors of the Federal Reserve System, the Federal Deposit Insurance Corporation, the National Credit Union Administration, the Office of the Comptroller of the Currency, the Consumer Financial Protection Bureau and the State Banking Regulators encourages financial institutions to “work prudently with borrowers who are or may be unable to meet their contractual payment obligations because of the effects of COVID-19.”
While these statements and orders may not be binding, they are instructive as to the current environment in which we expect lenders will appreciate the financial peril facing their borrowers and the need to work together to formulate creative and collaborative solutions to ensure their financing arrangements do not present an impediment to recovery.
Here are some good examples of borrower-friendly amendments that could be managed by Fintep’s integrated amendment management tool that will help borrowers to stay afloat until the economy gets up and running again:
a. Defer interest payments or interest being paid-in-kind, in each case, until a specified date.
b. Defer principal amortization payments or principal being paid-in-kind, in each case, until a specified date.
c. Consider deferring 2020 administrative agency and collateral agency fees and other fees on a deal-by-deal basis.
d. Waive or defer 2019 and 2020 Excess Cash Flow mandatory pre-payments.
e. Consider waiving or deferring other mandatory pre-payments on a deal-by-deal basis.
f. Consider modifications to the disposition of assets provisions to permit, as applicable, (a) licensing intellectual property, (b) selling unused or obsolete inventory, (c) factoring receivables or other assets, and (d) accelerating settlements related to third-party claims.
g. Remove some restrictions around equity cures, such as allowing cures during the fiscal quarter and allowing for cures beyond the amount necessary to comply with financial covenants.
h. Exclude the impact of COVID-19 from the Material Adverse Effect definition so it is no longer a concern when bringing down representations in borrowing notices.
i. Consider building in the ability to participate in applicable government bailout programs such as SBA loans.
2.Financial Covenant Compliance
a. Reset financial covenant holidays or financial covenant levels, in each case, until a specified date.
b. Addition of EBITDA add-back for fees, costs, losses, charges, expenses and lost profits and revenues in connection with any natural disaster, pandemic, epidemic, disease outbreak, or other public health emergencies (including the Coronavirus Disease 2019 (COVID-19) or any similar or related disease caused by the SARS-CoV-2 virus), including any such items related to sourcing new supply chains.
c. Consider modifications to relevant Consolidated Net Income adjustments and/or EBITDA add-backs, as applicable: (a) extraordinary, unusual, one-time or non-recurring fees, costs, losses, charges and expenses, (b) fees, costs, losses, charges and expenses relating to facility or operational shutdowns, (c) business interruption and other insurance proceeds, (d) reimbursable or indemnifiable costs, losses, charges, and expenses, (e) restructuring fees, costs, losses, charges, and expenses, (f) cost-savings initiatives, (g) goodwill impairment and (h) lost profits and revenues.
3.Financial Reporting Requirements
a. Extend the deadline for delivery of 2019 and 2020 audited financial statements (and related compliance certificates on a deal-by-deal basis) and, if necessary, revise credit agreement to permit a going concern qualification relating to COVID-19 effects.
b. Extend deadline for delivery of 2020 quarterly and/or monthly unaudited financial statements (and related compliance certificates on a deal-by-deal basis).
4.Events of Default
a. Waive default interest or convert default interest to be paid-in-kind.
b. Waive the cross-defaults to certain indebtedness, if applicable, or material agreements.
c. Review all Events of Default to determine whether any other default waivers are needed.
5.Eligible Assignees; Loan Assignments and Participations
a. Prevent lenders from assigning or participating the loans (including to a “Disqualified Institution”) without the borrower’s prior written consent.
b. If not already included, amend credit agreement to provide the ability for private equity sponsors to purchase the loans subject to customary caps and voting limitations.
6.ABL Facility Considerations
a. Reduce excess availability or liquidity triggers due to greater-than-expected cash outlays due to COVID-19 expenditures.
Fintep has several ways of elevating the agency position by improving the workflow. Functions like Document sharing, digital waiver and amendment management, and customized live deal pages with the agent, lender, and borrower views. The tool we offer handles communication exchange between agents and participants more efficiently.
If you are interested in all we offer to improve the workflow within the syndicated loan processes, you can find more information in the website or contact us!
Fintep’s ensured that our syndicated loans caught up with the other asset classes thanks to shorter settlement cycle and more efficient processing. Ultimately, Fintep reduced our exposure to risk, increased our operational margins by 57% and improved our customer service.
We can now compare covenants across similar deals, capital structures, and filter according different events; this was simply impossible to do it before.
It is an interesting and very powerful platform. I haven't seen anything on the market that is nearly as complete as this.