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The biggest challenge information technologies have encountered is quantifying abstract concepts. It is the case of credit ratings; it is hard to determine how safe it is to assume a credit will be payed back, but the real challenge is to explain and understand the reasoning behind the conclusion. With the research we group 41 have made, we can propose a solution to explainable credit ratings thanks to machine learning and and data processing, not only allowing enterprises to avoid overly risky inversions, but to save SMEs from unjust denial of investment, also creating a standard score for all small business to try and stand up to.

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If you care about credit scoring, you will know that one of the biggest problems nowadays is knowing which companies are more likely to pay back their loans. There are a lot of ways that this impacts your daily life, for example, the cost of borrowing money or housing. Mortgage costs can be sky high, and you might wonder what can you do to lower these costs. We are a group of computer science students concerned with this and we have made a system with these issues in mind that uses the J48 algorithm based on increasingly specific rules to give us the best solutions possible and make it comprehensible for everyone. With our algorithm you can solve your problems and analize different entities' credit scores, how they came to be and what you can do to upgrade them with the best accuracy in the market.

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Hi everyone — we’re Zhen, Haojie, Panos, and Laura, and we’re working on making credit scoring fair and transparent.

Right now, getting a credit score today can feel confusing and unfair.

You’re told “yes” or “no,” but never why.

Our project fixes that. We’re designing a credit scoring system that rates people from AAA to D and clearly explains why they got that rating and how they can improve it.

Unlike most models, ours can adapt to real-world changes — things like inflation, market shifts, or new regulations. It also works across languages and can use news and external data to stay accurate and fair.

No more mystery scores or unfair surprises — just clear, adaptable, and trustworthy credit decisions that put people back in control.

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The Elevator Pitch structure is really good. At the begining they describe the main problem, they also give a practical example using Ebury as subject, moreover this group demonstrates how well they understand the topic. However, the pitch could be stronger with a more engaging opening to capture attention early.

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The opening is weak. “One of the biggest problems…” is too generic

Unclear solution: “We made a system” doesn’t say anything. In one short sentence, say what it does and how. Example:

They are very short specific

they mention that they have a lot of background- why should I trust them?

They don’t comment on their solution, just say that with their algorithm we can solve our problems, but why or how would I solve them?

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We think it's a pretty good work, the elevator pitch is very good written and with a very rich vocabulary in the topic. But it feels a bit long and repetitive about FX exposure.

However, it is an impressive first elevator pitch. Congrats! :)

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Sales teams waste a lot of time trying to find SMEs that really deal with FX exposure. The problem is, most of these companies don’t show up in normal databases. But if you look at public data, you will be able to find thousands of them importing, exporting, and sending money abroad, they’re just hard to spot. Our system fixes that: it uses natural language processing to find those hidden FX signals and ranks each company by how much revenue potential they have, making this task more efficient and  simple.

We are Group 31, and our solution to the main challenge is to create an automated lead discovery tool that uses public data and various natural language processing techniques to identify SMEs with high potential FX exposure. By analyzing data from company websites, directories and news articles, we can detect keywords and diverse patterns related to international trade, such as imports, exports, or other cross-border operations. Later on, we apply a model based on grading and scoring each company's FX revenue potential to help financial enterprises prioritize leads and improve the efficiency of their sales outreach.

This way, we offer a solution that delivers the best results by identifying the most interested clients based on their key interests achieving greater accuracy than traditional methods. We’re looking for partners to collaborate on our automated lead discovery tool and validate its ability to identify and rank SMEs with foreign exchange exposure using real market data.

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Engaging, relatable and distinguished. The conclusion is a bit redundant but it does its job by finishing the pitch.

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Strong pitch. The intro grabs attention and makes the problem easy to relate to. The idea of turning “computer says no” into something human is great. Maybe trim a bit in the middle where you mention external data and macro shocks so it so it’s easier to follow. Overall, super clear and engaging.

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Your pitch clearly explains the problem, solution, and benefits. The teacher analogy is helpful. To improve, try shortening some long sentences and ending with a strong impact statement. Overall, it’s understandable and engaging :)

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You explained a good and specific solution, however i think u should emphasize more on the objective/problem you want to solve, and the reason why. Keep going guys, you did a great job !

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The text is great excluding the fact that you do not explain why your solution is different from the rest. You did a great job explaining the proyect and creating the need :)

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Hi, we are Group 32, and we’ve identified that many companies struggle with effectively segmenting their clients, which is critical for maximizing value and meeting specific needs. Without clear segmentation, businesses risk missing opportunities, misallocating resources, and failing to provide personalized solutions.

A prime example of this is Ebury, a company that serves a diverse range of clients, from small businesses to large corporations with complex currency needs. As their client base grew rapidly, their one-size-fits-all approach became less effective. To address this, we propose a solution using data-driven segmentation, applying machine learning techniques to analyze transaction behaviors, client size, risk appetite, and growth potential. This approach will help businesses like Ebury better understand their clients, optimize resources, and create tailored solutions that enhance client satisfaction and drive profitability.

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One of the biggest problem that banks are facing nowadays is knowing which companies are more likely to pay back the loans. You might think how credit scoring impacts you, and indeed there are a lot of aspects, for example, the cost of borrowing money or housing. Mortgage costs can be sky high, and you might wonder what can you do to lower these costs. We are a group of student with a lot of background and we have made a system with this issues in mind. With our algorithm you can solve your problems and analize different entities' credit scores, how they came to be and what you can do to upgrade them.

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Have you ever wondered what your clients have in common? Would you like to optimize your bussiness procedure?

We are team 26 and we have developed and studied how you can maximize your efficiency by using cutting-edge technology like Machine Learning Algorithms and over 1 million data.

We distinguish ourselve by the use of scalable technologies and refined algorithms rather than the more traditional approach of using simpler algorithms that are now redeemed unusable because they cannot manage huge datasets.

Thank to our services, our clients have experienced an improvement of around a 30% increase in benefits. Due to client clustering and segmentation we were able to identify focus points and potential markets and clients.

In conclusion, we are a compromised team focused on creating long lasting relationships with our clients by using top-notch and trustworthy technologies.

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It is common that almost every single company want to obtain extra information about their clients in order to use it on their favour, and that is the reason why we are here. Hello we are group 34 formed by Moisés Luján, Laura Chapado, Juan Bretones and Mendo Urraca.

We look at our client's behaviour using revenue, transactions, orders and products. From there, we create clear, human segments with simple profiles and revenue per client. Like a great teacher who adapts to each learning style, we adjust the offer, the timing, and the channel so every message feels relevant, not random. We use simple shopping signals like last purchase, how often they buy, how much they spend, and their usual product mix, and we apply K means, hierarchical clustering, and DBSCAN, reduce noise with PCA, and check quality with the silhouette score. The result is better targeting, less waste, and higher sales and customer value over time.

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Hello, we are group 47 - Klaudia Pietras, Leila Gómez, Alba Pintado, Andrea Avilés.

Our project tackles Challenge 2: Automated Lead Discovery.

Today, banks and financial institutions struggle to identify small and medium enterprises (SMEs) that have foreign exchange (FX) exposure and potential for new business. This process is often manual, slow, and incomplete.

Our solution automates this discovery using natural language processing (NLP) applied to public data sources. It detects SMEs likely to operate internationally, then ranks them by their revenue potential, helping financial teams prioritize the most promising leads.

Think of it like a teacher spotting which students are ready for a debate, our system recognizes which companies are ready for FX solutions.

By turning unstructured data into insights, we empower institutions to uncover hidden opportunities, boost efficiency, and stay ahead in a competitive market.

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Hi everyone — we’re Zhen, Haojie, Panos, and Laura, and we’re working on something that affects almost everyone.

Imagine applying for a loan and only getting a number back — no explanation, no clue what it means, or how to change it.

That’s how most credit scoring systems work today: opaque, static, and hard to trust.

We’re building a fair and transparent credit scoring model that rates users from AAA to D, but actually shows how the score was made and how it can improve.

Unlike most models, ours aims to adapt in real time to macroeconomic shocks — things like inflation, interest rate hikes, or sudden market shifts — so the system stays fair even when the economy changes.

We also want our model to be understandable across languages and to draw on relevant external data, such as financial news or updated risk reports, to make smarter and more timely decisions.

In short, we want to make credit scoring transparent, adaptable, and human — turning “computer says no” into “here’s why, and here’s what you can do.”

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When lending money to businesses, there are many big challenges; one of the biggest is to understand the credit risk.

This risk is not only about the business’s balance; it's about analyzing the company’s cash flow, such as imports and exports, market flow, or the credit ratings in a country. Some businesses decide to ignore this problem and just lend money to "random businesses," and most of them end up in a very bad financial situation.

We are Group 27, and we have studied this problem by analyzing this data and working with AI. We present our solution: Our financial AI is trained with diverse datasets from emerging markets to identify not only high-risk borrowers, but also strong, reliable businesses that represent good investment opportunities

Our system combines large-scale financial and non-financial data to deliver accurate credit predictions with a very small margin of error. This makes it easier for lenders to understand how credit risk truly works and decide where their money will create the greatest impact.

In conclusion with our AI you are able to understand how this credit risk works and where to lend your money.

Thank you!

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Elevator Pitch (modified after revision) - Group 46

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Elevator Pitch (modified after revision) - Group 46

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Hello, we are Group 46 — Marcos Casado, Ricardo Terol, Adrián Zazo, and Víctor Pérez.

We’re here to present our proposal for Challenge 1, which focuses on predicting default risk based on credit ratings.

Our approach explores and compares several algorithms described in recent research, with the goal of selecting the most accurate model and improving it through our own optimizations.

Unlike many existing solutions that rely on a single predefined method, our system combines multiple approaches and adapts dynamically to different types of clients and credit profiles. This allows us to achieve higher prediction accuracy and better generalization across real-world data.

By developing a reliable and adaptable algorithm to evaluate potential clients, we help companies optimize client selection and avoid funding unreliable applicants.

This is essential, since making credit decisions without robust risk assessment can lead to major financial losses. Our solution offers a smarter, data-driven way to choose the right clients with confidence.

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You are pretty clear with what your solution consists of, but you might need to reinforce how you express why your solution is better to highlight it

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Creemos que os falta decir quienes soys

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Good evening, we are group 37 and we investigate credit scoring, a topic we have researched about, learning the state of the art techniques and their flaws and effectiveness. When a credit default prediction system fails it becomes a critical threat to the company. There already exist accurate solutions like neural networks, but they are not quite explainable, which is required by law. We propose a hybrid of the best techniques commonly used, neural networks and random forests. By predicting the default rate using a modified random forest created using the input from a small neural network, we keep the explainability of the final random forest and improve its accuracy similar to a neural network.

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We all struggle with data, especially when it comes to analysing huge amounts of it.

Imagine having to deal with this problem every day. That is what most businesses need to do.

To help them (and each other too), we present an innovative way of classifying information into segments.

This will make it easier to find regularities, temporal trends or outliers in client databases.

We focus our work on companies interested in data analysis with an economic goal, as we are a team of computer science students passionate about turning data into strategic knowledge.

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There exists a difficulty in evaluating the credit reliability of small and medium enterprises, especially those with limited or inconsistent data.

To overcome this challenge, we have developed an explainable AI credit scoring system that integrates both internal and external data to accurately estimate the probability of default.

Unlike traditional black-box models, our system provides transparent and interpretable results, expressed through a clear AAA–D rating scale. It not only predicts credit risk but also explains why, by revealing the key factors behind each score. This makes every decision traceable, reliable, and easier to trust.

We are a team of four engineering students with strong expertise in coding, finance, and artificial intelligence — united by the goal of making credit assessment more transparent, fair, and data-driven.

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Just in Spain alone, more than 99% of enterprises are SMEs. These types of enterprises tend to be the ones with the highest need for credit, and they also happen to be the ones to find more difficulties obtaining such credits because of the lack of certainty of them having the means to back these credits up, and their huge disadvantaged position facing multinational companies with bigger access to financial resources. That’s why, for the sake of Spain’s economy, we have developed a solution to empower these kinds of companies while providing financial institutions with information and certainty that their credits will be returned. We are group 39, a group of Computer Science and Business Administration students motivated to find solutions to problems where technology and business converge. We propose a solution based on ML and GAI with a deep learning model, as well as financial background to come up with a credit ranking system similar to those we already know of (AAA-D) that will predict the credit default risk of SMEs. Our solution will make sure that in a world constantly evolving, SMEs do not get left behind.

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Nowadays one of the biggest challenges fintech companies face is to find and retain clients. It can be tedious to guess which business deals can be most beneficial for your company. The amount of data out there is overwhelming and difficult handle manually. That’s where we come in. We’re group 43 and we have built a solution for this very problem: Automatic Lead Scoring, which is a fast, accurate and reliable lead scoring system. Honing the computational power of AI, machine learning and web-scraping we generate a ranking by scraping public data resources, websites, registries, reports, and so on. Additionally, thanks to our NLP model, the system will print out easy and understandable outputs. The days of manual cross-referencing are over; we aim to transform the way business discover and prioritize new clients.

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Dealing with clients who default is one of the biggest struggles of managing a financial company. Some companies develop their own credit scoring systems to prevent that, but most of them are private or can be difficult to understand for non-technical users.

We are Group 53, and we have developed a tool to help financial companies make better decisions.

Our solution relies on public information and previous operations to ensure the information and the reasoning behind our AI model is available to you at any time. We also offer the possibility to guide the software, giving you full control of the important financial decissions made by our software.

To sum up, we offer your company an easy-to-use credit scoring solution that does not compromise on transparency and user adaptability.

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Hello, we are Group 46 — Marcos Casado, Ricardo Terol, Adrián Zazo, and Víctor Pérez.

We’re here to present our proposal for Challenge 1, which focuses on predicting default risk based on credit ratings.

Our approach explores and compares several algorithms found in recent research papers, aiming to identify the most effective model and understand how it can be improved.

By developing a reliable algorithm to evaluate potential clients, we can optimize client selection and reduce the risk of funding unreliable applicants.

This is crucial, because making financial decisions without accurate risk assessment can lead to significant economic losses. Our solution helps ensure that companies make data-driven, trustworthy decisions when granting credit.