What do we do?

End-to-end Solution

Fintep provides an IT platform that automates and manages the loan lifecycle from processing to collection

Realtime Monitoring

Boost process standards and monitoring: enhance automation and business process management

Centralized Capabilities

Improve connectivity between different stakeholders and eradicate silo-based systems with partial integration capabilities that cause data redundancy and delayed loan processing

Automated Workflows

Eliminate monotonous, vain tasks such as manual gathering of data, filling out multiple forms and periodic request of deal documents

Transparency across transactions

Maintain a unique monitoring system across all transactions to encourage transparency and avoid disputes

Superior Insights

Avoid usage of excel based analysis clauses and unnecessary delays in calculating risk exposure events. Structure the right loan solution to each client

One Product for everyone

Fintep is a one-stop solution for every level within your organization

  • Closing the Loan

    • Present the status and progress of the Loan
    • Review Loan Book by Country, Risk, Industry, Agencies and Entity exposure
    • Present main impacts on Loan Book in terms of efficiencies, reduced time-to-market, agility or increased flexibility
    • Avoid third party expensive Data Rooms
  • Portfolio Managment

    • Monitor project execution and KPIs; review risks and set up correction measures to ensure fulfillment of global objectives
    • Make key decisions and escalate issues if necessary
    • Ensure coordination across work streams and timely participation of all stakeholders
    • Eliminate unnecessary expenses such as lawyer Fees and Internal Costs to run Amendments/Waiver/Variation Order approvals

Irrefutable Numbers

5

managed loans

50.000

deals real-time managed

120

funds & banks as lenders

32

operating jurisdiction

About us



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.

42000


Contacts

Take advantage of our powerful data base.

200


Happy Users

Reliable and certified Software.

100


Data Points per Loan

Enrich your Portfolio with Fintep Experience.

10000


Cups of coffee

Benefit of more than 200.000 hours of R&D code lines.

Available Plans

Essentials

Out-of-the-box Amendment management

$35*

  • Deal Backlog
  • Mobile
  • Communication Deal Board
  • Security
  • Amendment Voting

Enterprise

Customizable monitoring

$85*

  • Reporting & Data Analytics
  • Semantic Reading
  • Real Time Monitor
  • Contract Version
  • Computer Vision

Professional

Virtual Data Room

$70*

  • Document Repository
  • Document Management
  • Extended User Access Privilege
  • Multitenant Support
  • Drag & Drop all/any document type.

*Per deal per month, billed annually

Feeds

Automating the bank’s back office

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.


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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!!!

Architectural Approaches of Federated Machine Learning in Investment Banking

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.

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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.
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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.



Privacy and machine learning.

Privacy and machine learning. 
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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.

Testimonials

  • 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.

    Chief Operating Officer, Global Investment Bank
  • We can now compare covenants across similar deals, capital structures, and filter according different events; this was simply impossible to do it before.

    Managing Director, Portfolio Monitoring, Corporate and Investment Bank
  • It is an interesting and very powerful platform. I haven't seen anything on the market that is nearly as complete as this.

    Senior Analyst, Leveraged Finance, Credit Hedge Fund