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 dream of achieving rapid, large-scale process automation is becoming a reality for some banks. Competitors cannot afford to miss the opportunity to transform their own back-office processes.
Banks have enhanced many of their customer-facing, front-end operations with digital solutions. Online banking, for example, offers consumers enormous convenience, and the rise of mobile payments is slowly eliminating the need for cash. But too many processes at banks still rely on people and paper. Often, back offices have thousands of people processing customer requests.
This high degree of manual processing is costly and slow, and it can lead to inconsistent results and a high error rate. IT offers solutions that can rescue these back-office procedures from needless expense and errors.
There is a significant opportunity exists to increase the levels of automation in back offices. By reworking their IT architecture, banks can have much smaller operational units run value-adding tasks, including complex processes, such as deal origination, and activities that require human intervention, such as financial reviews.
IT-enabling operations encompasses both automating processes (preventing customers from using paper, digitizing work flows, and automating or supporting decision making) and using IT solutions to manage residual operations that must be carried out manually (for example, using software for resource planning). By taking full advantage of this approach, banks can often generate an improvement of more than 50 percent in productivity and customer service!!!
Thanks to Fintep, some banks are already taking steps toward harnessing the considerable potential of this opportunity. For example, one of our existing customers, a large universal bank categorized its 900-plus end-to-end processes into three ideal states: fully automated, partially automated, and “lean” manual. This bank determined that 85 percent of its operations could—theoretically—be at least partially automated. At the time of this analysis, fewer than 50 percent of these processes were automated at all.
This scenario sounds promising and Fintep is happy to help you to achieve this task!!!
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.
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.