Tag: Alternative credit

Our 2021 in Review at Cardo AI

2021 Review – What happened this year at Cardo AI

Our 2021 in Review at Cardo AI

2021 review – What happened this year at Cardo AI

As we are starting the new year, we took a look back at what we were able to accomplish during 2021. January is an ideal time to reflect on what we’ve achieved so far and what we expect to do in the months ahead. 

Keep on reading to discover more about how 2021 has been like here at Cardo AI!

Our Key Moments

2021 has been a big year for Cardo AI: we have reached amazing milestones and achievements that have brought us closer to our main mission: revolutionizing the private debt market with technology powered by artificial intelligence. 

Let’s look at some of the major accomplishments of the year:

Our business results

Our technology has helped clients to make smarter decisions, as they are able to analyse quickly more data and gain better insights, lower their operational costs, and further scale their operation. With the same team, our clients are now able to manage 2.5x more assets while encountering 95% less errors. 

  • 30+

    # Transactions

    Alternative Funds, Sub-funds, SPVs
  • 3Bn+

    € Amount

    SME loans, Consumer Loans, Trade Receivables, PA Loans, etc.
  • 600K+

    # Loans

    35 countries, 22 sectors

Becoming a PRI signatory

  • We are 100% committed to promoting responsible investment

    Our mission is to support our clients in having a real influence on sustainable investing and integrate ESG elements into the private debt market. One of the ways we hope to accomplish this is through our future products, which will allow managing ESG scoring and rating data from both external and internal sources. Find more by reading our article about becoming a PRI signatory.

  • Cardo AI becomes a PRI signatory

Talent & People

One of the things we really are proud of is the fact that we’ve had more than 40 new joiners this year, doubling our team compared to 2020. Both senior and junior talents have joined our teams for Business Analytics & Development, Data Science &  Engineering, Financial, Marketing, and Software Engineering. 

  • Reaching 50 talents

    Back in 2018, we started CARDO AI with a small group of people and the mission to bring technology into the private debt market. In September 2021, we reached 50 amazing talents working across three different countries and helping solve the biggest challenges in private debt!

  • Opening new offices

    As we have welcomed many new employees, we also needed to expand our working spaces. We opened a new office in Tirana, Albania (where we now have two) and we moved to a bigger office in Milan, Italy. 

Benefits and initiatives

In 2021 we introduced several new initiatives and benefits for our talents, with the objective of further improving our working environment and making Cardo AI a great place to grow and develop your career.

  • Work-Life balance officer

    We want to make sure that each employee has the perfect environment to thrive & grow in his chosen career. 
    That is why we appointed a Work-Life Balance Officer who, alongside his activities as Growth Manager, will ensure a healthy work-life balance at Cardo and find ways to mitigate and train people in managing stress, burnout, and overtime work. 

  • Remote and Flexible Working

    At Cardo we give team members maximum flexibility to choose the setup and schedule that works best for them, whether that’s at home, at one of our offices, or at another location. We truly believe that giving our employees the freedom to choose where and when they work best can boost long-term motivation, happiness, and overall productivity.

  • Stock option plan

    With our stock option plan we give the opportunity to employees  to become part of the ownership of the company and become a real shareholder of Cardo. 

  • Relocation Package

    We give the opportunity to employees who have been with us for more than a year to relocate to their office of choice, be it Milan, Tirana, or London!

  • Intrapreneurship in Cardo AI

    “Intrapreneurship in Cardo AI” means that our team members can propose new ideas to launch new products, features, or new technologies. If the idea is selected, a side development team is created to come to MVP and production stage.

  • Cardo AI Startup Incubator

    With this initiative we plan to select 2/3 ideas per cohort and everyone that wants to follow any of the startups is free to do so. At the end of the program, both Cardo AI and everyone from our team can invest in the startups, so we all become entrepreneurs.

  • Cardo Kickstart Training Program for recent graduates

    Cardo Kickstart

    We launched the second edition of Cardo Kickstart, a program aimed at supporting fresh graduates in their transition from university halls to the labor market in Tirana.  

Company Trips

We were able to organize two amazing company trips – a great opportunity to connect with each other, relax and recharge for the upcoming challenges! team building and getting to know each other more. Here are some pictures of our two trips, the first one to Drimades, in June 2021, and the second one to Theth, in September. 

One year of tech innovation

  • Virtual Data Room

    We have integrated a VDR system into our Securitization platform, allowing our clients to securely store critical papers, contracts, and data that they are willing to share with a third party.

    Thanks to Cardo AI’s VDR, originators and arrangers of securitizations are now able to:

    • Ensure quality, accessibility, and reliability of data in all the stages of the transaction.
    • Offer to potential investors a fully digital Due Diligence experience.
    • Keep data always up-to-date to grant full transparency.
  • Marti the Chatbot

    With increasing interest and research on Artificial Intelligence (AI), Natural Language Processing (NLP), and machine learning, bots are becoming progressively more efficient. In the fintech market, bots can support the user along its journey on applications, programs, and software. 

    For this reason, in 2021 we launched Marti, Cardo AI’s virtual assistant. Currently, the chatbot is integrated into our digital lending product to guide our users and help them navigate the platform, making it very fast and easy to access information and gain insights on private debt investments and operations.

  • IDP

    IDP is a service that handles Authentication & Authorization, in a focused way, in a cluster infrastructure. This way, we can have a single UserBase for different applications or services while they focus more on providing features and functionalities.

    Some of the main features it offers are:

    • Single Sign-on – This allows a user to access multiple applications in the cluster with the same set of credentials.
    • Granular permissions structure – Roles, Functionalities, Permission.
    • Default Deny – This means that explicit permissions have to be given to each user for everything that they can access.
    • Temporary user access – This feature allows the creation of temporary tokens.

Looking ahead

There are many initiatives we want to carry out this year, starting with the first one in January: The Women in Tech event.

It will be an online event with the objective of underlining females’ contribution to the tech industry, their participation in the tech community along with the challenges they face in becoming part of it. In addition, we want to give emphasis to female entrepreneurship and leadership in technology. Discover more on the LinkedIn page of the event.

We are very proud of everything we accomplished in 2021, and we can’t wait to work towards new goals and achievements in 2022!

Want to stay updated about our initiatives and products? Don’t forget to follow us on Linkedin!

About the author

Altin Kadareja

Altin Kadareja is the CEO and co-founder of Cardo AI. Prior to founding Cardo AI, Altin has covered several investment and risk management roles at BlackRock, Prometeia, Intesa Sanpaolo and Allianz Bank Financial Advisors across different European markets. He holds a master of science degree in Economics and Management of Innovation and Technology from Bocconi University in Milan and followed an executive program in Risk Management at Imperial Business School in London.

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Office employee doing Financial Data Extraction from Statements with Machine Learning

Financial Data Extraction from Statements with Machine Learning

Data is the foundation that drives the whole decision-making process in the finance ecosystem. With the growth of fin-tech services the process of collecting this data is more easy accessible, and for a data scientist becomes necessary to develop a set of information extraction tools that would automatically fetch and store this relevant data. Doing so, we facilitate the process of financial data extraction, which before the development of this tools was done manually, a very tedious and not very time-efficient task.

One of the main providers of this “key knowledge” in finance is the Financial Statements, which offer important insight for a company through its performance, operations, cash flows, or balance sheets. While this information is usually provided in text-based formats or other data structures like spreadsheets or data-frames (which can easily be utilized using parser-s or converters), there is also the case when this data comes as other document formats or images in a semi-structured fashion, which also varies between different sources. In this post we will go through different approaches we used to automate the data extraction from these Financial Statements that were formerly provided from different external sources.

Financial Data Extraction: Problem introduction

In our particular case, our data consisted of different semi-structured financial statements provided in PDFs and images, each one following a particular template layout. These financial statements consist of relevant company-related information (company name, industry sector, address), different financial metrics, and balance sheets. For us, the extraction process task consists of retrieving all the relevant information of each document for every different entity class and storing them as a key-value pair (e.g. company_name -> CARDO AI). Since the composition of information differs between documents we end up having different clusters of them. Going even further, we observe that the information inside the document itself represents itself through different typologies of data (text, numerical, tables, etc.).

Sample of a financial statement containing relevant entity classes

In this case, two main problems emerge: we have to find an approach to solve the task for one type of document, and secondly by inductive reasoning, form a broader approach for the general problem, which applies in the whole data set. We have to note here that we are trying to find a single solution that works in the same way for all this diverse data. Treating separately every single document with a different approach denotes missing the whole point of the present task at hand.

“An automated system won’t solve the problem. You have to solve a problem, then automate the solution”

Methodology and solution

Text extraction tools

Firstly we started with text extraction tools like Tabula for tabular data, and PDFMiner and Tesseract for text data. Tabula scrapes tabular data from PDF files, meanwhile, PDFMiner and Tesseract are text extraction tools that gather the text data respectively from PDF and images. The way these tools work is by recognizing pieces of text on visual representations (PDFs and images) into textual data (document text). The issue with Tabula was that it worked only on tabular data, however, the most relevant information in the financial documents that we have is not always represented in tabular format.

Meanwhile, when we applied the other tools, PDFMiner and Tesseract, the output raw text was completely unstructured and non-human-unreadable (adding here unnecessary white-spaces or confusing words that contained special characters). This text was hard to break down into the meaningful entity classes that we want to extract from there. This was clearly not enough so we had to discover other approaches.

GPT-2

Before moving on, we made an effort to pre-process the outputted text from the above-mentioned extraction tools, and for that, we tried GPT-2 [1]. GPT-2 is a large transformer-based language model with 1.5 billion parameters developed from OpenAI and was considered a great innovative breakthrough in the field of NLP. This model, and also its successor – GPT-3, has achieved strong performance on many NLP tasks, including text generation, translation, as well as several tasks that require on-the-fly reasoning or domain adaptation. In our case, we tried to exploit one of its capabilities which was text summation. After getting a considerable amount of text from the previous text extraction tool, we tried to summarize all this information using the GPT-2 model and take out non-relevant information, taking advantage of the attention mechanism of the transformer model. But this approach did not seem to work quite well considering the non-structured text which is very hard to summarize. Apart from that, there would always be the possibility of the model removing the important information from the text and we cannot give it the benefit of doubt in this regard.

Bounding boxes relationship – OpenCV

The unpromising results of the above approaches made us entertain the idea of treating it as an object detection task using computer vision. Object detection is done by means of outputting a bounding box around the object of interest along with a class label. Then we could construct a relationship graph between these “boxed” entities [2] (see image above). Going forward with this method we tried to do the same with our documents, but instead draw boxes around text that represents an identifiable entity and label each box with the entity name it contained. The next step would have been to develop an algorithm that calculates a metric that represents the relationship values between these boxes based on their spatial position. We could then train a machine learning model that would learn from these relationship values and sequentially decide the position of the next entity by knowing the document locations of the previous ones.

The model creates a relationship graph between entities

However, that was not an easy task, due to the fact that it is very hard to determine the right box which represents a distinct meaningful component in the report, and also as mentioned above different documents follow different document layouts and the position of the information we want to extract is arbitrarily positioned in the document. Henceforth, the previously mentioned algorithm might be inaccurate in determining the position of every box. We moved on to seek a better plan.

Named Entity Recognition

An example of NER annotation

Named-entity recognition (NER) is a sub-task of information extraction that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories.

During our research on the quest to explore new approaches for this task, we came upon the expression “named entity” which generally refers to those entities for which one or many strings, such as words or phrases, stands consistently for some referent. Then we discovered Named Entity Recognition, the task of locating and classifying words from a not annotated block of text into predefined categories. The desired approach to solving this task is by using deep learning NLP models which use linguistic grammar-based techniques. Conceptually this task is divided into distinct problems: detection of names and classifying them into the category they fall into. Hence we started to look for different implementations to design our language model.

NLP model – spaCy

At this point, our process path was pretty straightforward to follow due to the ease that the NLP libraries offer. And for this job, we decided to go with spaCy [3], which offers a very simple and flexible API to develop many NLP tasks, and one of them being Named Entity Recognition. The design pipeline could be conceptualized with the below diagram:

Solution pipeline

Before we start the design of our model we have to first construct the training data set. It will essentially consist of annotated blocks of texts or “sentences” that contain the value of the entity it represents and the entity name itself. For that, firstly we extract the text from the paragraphs where the desired information is present by making use of the previously used extraction tools. Then we annotate this text with the found categories, by providing the starting position and length of the word in the text. Doing so, we also provide some context to the model by keeping the nearby words around the annotated word. This whole information retrieved from the financial statements can then be easily stored in a CSV file. In SpaCy this would be represented with the below structure:

TRAIN_DATA = [    ("Cardo AI SRL is a fintech company", {"entities": [(0, 12, "Company")]}),    ("Company is based in Italy", {"entities": [(20, 25, "LOC")]})]

After we prepared our dataset, we then decided to design the NLP model by choosing between the alternatives that spaCy provided. We started from a blank non-trained model and then made an outline of the input and output of the model. We split the data into train and test sets and then started training the model, following this pipeline. From the training data, the text is firstly tokenized using the Doc module, which basically means breaking down the text into individual linguistic units, and then the annotated text inputted with the supported format is parsed with the GoldParse module to then be fed into the training pipeline of the model.

Training pipeline

Results and constraints

After training the model on about 800 input rows and testing on 200, we got these evaluations:

Evaluations

The evaluation results seemed promising, but that may have come also from the fact that the model was over-fitting or there was not a lot of variability in our data. After our model was trained, all we had to do was feed it with text data taken from the input reports after they had been divided into boxed paragraphs and expect the output represented as a key-value pair.

Constraints

Lack of data
– in order to avoid bias or over-fitting the model should be trained on a relatively large amount of data.
– acquiring all this data is not an easy process, adding here the data pre-processing step.
Ambiguous output
– the model may output more than one value per entity, which leads to inconsistency in interpreting the results.
Unrecognizable text
– the financial statements have poorly written text not correctly identifiable during the text data extraction tools recognition.
Numerical values
– having lots of numerical values in the reports it is hard to distinguish the real labels they represent.

Potential future steps toward better Financial Data Extraction

In recent years, convolution neural networks have shown great success in various computer vision tasks such as classification and object detection. Seeing the problem from a computer vision perspective as a document segmentation (creating bounding boxes around the text contained in the document and classifying it into categories) is a good approach to proceed on with. And for that, the magic formula might be called “RCNN” [4].

By following this path, we might be able to resolve the above-mentioned issues we ended up with our solution. Integrating many different approaches together may also improve the overall accuracy of the labeling process.

After the solution process is stable and the model’s accuracy is satisfactory we need to streamline this whole workflow. For a machine learning model, it is important to be fed with an abundant amount of new data which improves the overall performance and predicts future observations more reliably. In order to achieve that, it comes necessary to build an Automated Retraining Pipeline for the model, with a workflow displayed as the following diagram:

Workflow diagram

Conclusion

We went through and reviewed a couple of different approaches we attempted on solving the Named-Entity Recognition task on Financial Statements. And from this trial and error journey, it seemed that the best method was solving it using Natural Language Processing models trained with our own data and labels.

But despite seemingly obtaining satisfactory results in our case study regarding financial data extraction, there is still room for improvement. The one thing we know for sure is that the above Machine Learning approach provided us the best results and following the same path on solving this task is the way to go. Machine Learning is very close to reaching super-intelligence and with the right approach to present problems in every domain, it is becoming a powerful tool to make use of.

References

[1] Better Language Models and Their Implications

[2] Object Detection with Deep Learning: A Review

[3] spaCy Linguistic Features, Named Entity

[4] Ren et al., Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

EU Taxonomy: A practical guide for navigating troubled waters

EU Taxonomy Article A practical guide for navigating troubled waters

EU Taxonomy: A practical guide for navigating troubled waters

What is the EU taxonomy and what are its implications for financial market participants? In this article you fill find a practical guide on how to comply with the EU Taxonomy

What is the EU Taxonomy?

The EU Taxonomy is one of the main pillars of the EU’s Action Plan for Financing Sustainable Growth (2018), whose one fundamental aim is to reorient capital flows towards a more sustainable economy.

The Action Plan assigns private finance a pivotal role in reaching the EU’s ambitious goal of transitioning to a low-carbon, more resource-efficient, resilient, and competitive economy – in line with its commitment to fully implement the UN 2030 Agenda and the Paris Agreement, both being an integral part of its Green Deal.

However, it is posited that no shift of capital flows towards more sustainable activities can be truly achieved without a common definition of what “sustainable” means.

Through Regulation 2020/852 (“Taxonomy”), the EU establishes a unified classification system aiming at helping financial market participants (FMPs) channel investments towards financial products that truly pursue environmentally sustainable objectives, addressing, therefore “greenwashing” concerns.

The Taxonomy is complementary to the SFDR (Sustainable Finance Disclosure Regulation, Reg.2019/2088), in the sense that it mandates additional transparency requirements (both in pre-contractual, website, and periodic disclosures) in case financial products promote environmental characteristics (article 8 products) or pursue an environmental objective (article 9 products).

What does the EU taxonomy specify?

In particular, the EU taxonomy specifies that such financial products should first disclose to which of the following environmental objectives they contribute:

  • Climate change mitigation
  • Climate change adaptation
  • Sustainable use and protection of water and marine resources
  • Transition to a circular economy
  • Pollution prevention and control
  • Protection and restoration of biodiversity and ecosystems

Within its Final Report on the EU Taxonomy, the Technical Expert Group (TEG) recommends that investors should estimate Taxonomy-alignment separately for each of the environmental objectives for which substantial contribution technical screening criteria (TSC) have been developed. This means that it should be completed separately for climate change mitigation and adaptation (the objectives for which TSC are available as of now).

This is just one and the simplest step in assessing portfolio compliance with the Taxonomy. In the next section, we explore in detail the full process that has to be followed.

How does the Taxonomy work in practice?

As with all other regulations, nothing comes easy. This is particularly true in case investors have companies in their portfolios that are not subject to the EU Non-Financial Reporting Directive, such as non-EU companies and small-medium enterprises (SMEs). For such cases – which are not negligible especially for private debt investors – the TEG advises to follow a 5-steps approach:

Step 0 – Map in-use industry classification systems to NACE sectors eligible under the EU Taxonomy

A pre-requisite for estimating the Taxonomy-alignment of investment portfolios is the mapping of in-use sector classification systems (e.g. SIC, NAICS, BICS, GICS, ICB, RBICS, TRBC, etc) with the European industry classification system (NACE).

The Platform for Sustainable Finance (a permanent expert group of the European Commission) has elaborated a table providing an indicative mapping of selected industry classification systems, and how they relate to the description of economic activities in the EU Taxonomy Delegated Act adopted by the Commission.

Step 1 – Eligibility screening

Identify the companies whose turnover, CAPEX, or OPEX match the economic activities listed in the Taxonomy

For each entity, investors need to be able to assess the proportion of turnover derived from economic activities eligible under the Taxonomy (approx. 70 activities). If data can be obtained, investors should look also into CAPEX and OPEX. 

The turnover KPI (or revenue, if appropriate) is particularly relevant for the climate change mitigation objective. For climate change adaptation the assessment is rather more complicated (especially in the absence of reported data): for an activity to be eligible, there should be evidence that the entity has implemented tailored solutions to prevent physical climate change risks based on the performance of a vulnerability assessment.

It is recommended at this stage to also group eligible activities in two clusters:

  • economic activities that are “enabling” other activities to make a substantial contribution to one or more environmental objectives (e.g. the manufacturing of renewable energy equipment in the case of climate change mitigation);
  •  “transitional” activities, i.e. those activities for which there is no technologically and economically feasible low-carbon alternative (e.g. manufacturing of iron and steel), but that shall qualify as long as their technology is consistent with a 1.5C scenario (they “pass” certain technical screening criteria).

Economic activities such as electricity generation from Solar PV are considered to substantially contribute to climate change mitigation through their own performance.

Step 2 – Substantial contribution screening

Validate if the eligible companies meet the technical screening criteria (TSC) provided for the economic activity

This is likely the most difficult step to verify, especially in the absence of reported data. While some economic activities (e.g. electricity generation from wind) do not have technical thresholds to comply with, most of them have. As an example, electricity generation from geothermal is Taxonomy-eligible, but it should meet the technical criteria of no more than 100g CO2-e emissions per kWh over the life-cycle of the installation, as calculated using specific methodologies (e.g. ISO 14067:2018) and verified by a third party.

The final Delegated Act on climate objectives containing all the TSC has been published on the 9th of December. For ease of consultation, FMPs can use the Taxonomy compass.

The Taxonomy Regulation recognizes that in the absence of reported data, this step can be particularly burdensome. For this reason, it allows for complimentary assessments and estimates, as long as financial market participants explain the basis for their conclusions and the reasons for having made such estimates.

Step 3 – Do Not Significant Harm (DNSH) screening

Validate if the eligible economic activities do not significantly harm other environmental objectives

The Taxonomy Regulation mandates that once the TSC are deemed as satisfied, FMPs should check also that the economic activity that, e.g., contributes to climate change mitigation, does not significantly harm the other five environmental objectives. 

The third step requires investors to conduct due diligence to verify if the company’s activities meet some qualitative, quantitative, and process-based requirements for each other environmental objectives, not only at the production stage but over the life-cycle of the activity itself. 

Also here the lack of data could be a challenge for FMPs. The TEG recommends the reliance on existing credible information sources, such as reports from international organizations, civil society, and media, as well as established market data providers.

Step 4 – Social minimum safeguards screening

Validate if companies meet minimum human and labor rights standards

The Taxonomy mandates that for economic activity to be environmentally sustainable, it should also be aligned with the OECD Guidelines for Multinational Enterprises, the UN Guiding Principles on Business and Human Rights, the International Labour Organisation’s (ILO) Core Conventions, and the International Bill of Human Rights. As for step 3, the TEG recommends relying on internal due diligence processes as well as on external credible information sources.

Step 5 – Calculate the alignment of the investment with the Taxonomy 

Economic activity is to be considered Taxonomy-aligned only if it complies with steps 1-4. Once the aligned portions of the companies in the portfolio have been identified, investors can calculate the alignment of their funds with the taxonomy (as an example, if 10% of a fund is invested in a company that makes 10% of its revenue from Taxonomy-aligned activities, the fund is 1% taxonomy-aligned for that investment, and so on).

FMPs can find use cases studies on the application of Taxonomy requirements for several asset classes available on the PRI (Principles for Responsible Investment) website.

What is the relationship between the SFDR and the EU Taxonomy?

As stated previously, the Taxonomy Regulation is complementary to the SFDR, since it requires additional disclosure requirements for FMPs in case they market financial products promoting environmental characteristics (article 8) or the attainment of an environmental objective (article 9).

As Regulation 2019/2088 mandates, Taxonomy-alignment disclosure of financial products it’s not only due in pre-contractual documents (article 8, 9) but also on websites (article 10) and through periodic reporting (article 11). 

An important point to underline is that website disclosure shall provide, for each art.8 and art.9 product, “information on the methodologies used to assess, measure and monitor the environmental or social characteristics or the impact of the sustainable investments selected for the financial product, including its data sources […] and the relevant sustainability indicators”. In case such products have an environmental focus, there should be also disclosure on the methodology used to estimate Taxonomy-alignment.

Last but not least, DNSH screening for Taxonomy-aligned products should not be confused with PASI (Principal Adverse Sustainability Impacts) reporting, due at entity level pursuant to article 4 SFDR, and at product level pursuant article 7.

Article 4 demands FMPs exceeding 500 employees or stating considering “principal adverse impacts of investment decisions on sustainability factors” to publish on their websites a description of such impacts. The ESAs (European Supervisory Authorities, EBA, EIOPA, ESMA) have developed draft Regulatory Technical Standards (RTS) supplementing Reg.2019/2088, according to which financial undertakings will have to disclose on their websites selected aggregate ESG metrics (approx.20) estimated across all investee companies. Companies with less than 500 employees not considering adverse impacts on sustainability factors of investment decisions will have to publish as well a clear motivated statement for not doing so. In both cases, FMPs will have to disclose relevant information by 30 June 2023.

What are the deadlines for reporting Taxonomy alignment?

The SDFR started applying on 10 March 2021 and the Taxonomy from 1 January 2022. However, the design of the Regulatory Technical Standards – which provide the detailed requirements for pre-contractual, website, and periodic disclosure pursuant to both the SFDR and the Taxonomy – has proven longer than expected. 

In an effort to jointly develop RTS for both the SFDR and the Taxonomy, the European Commission has postponed the application of the Delegated Act containing the RTS to January 2023.

However, financial undertakings subject to an obligation to publish non-financial information pursuant to Article 19a or Article 29a* of Directive 2013/34/EU, shall start disclosing the proportion of Taxonomy-eligible activities within their portfolios from January 2022. Full disclosure of Taxonomy-aligned activities will be required instead from January 2024. Furthermore, Reg.2021/2178 clarifies that exposures to national and supranational issuers including central banks shall be excluded from the calculation of Taxonomy KPIs altogether, while derivatives and exposures to undertakings are not subject to non-financial disclosure regulation (e.g. SMEs) shall be excluded only from the numerator.

It should be borne in mind that such a timeline applies only to matters regarding the publishing of non-financial statements. FMPs considering adverse impacts on sustainability in their investments (PASI) and/or marketing article 8 and 9 products, should follow closely the developments linked to the Delegated Act containing the Regulatory Technical Standards.

(*) Article 19a and 29a pertain to non-financial disclosure requirements for large undertakings

For more details and recommendations on EU, Taxonomy implementation does not hesitate to contact us.

About the Author

Cristina Hanga

Cristina is the ESG Expert at CARDO AI, working across the company’s product suite.

Prior to joining CARDO,  she has worked as an ESG Analyst at Sustainalytics, where she was Lead Quality Control for the Consumer Goods sector, and contributed to several methodology developments. Cristina has also spent a period in KPMG, where she advised companies on ESG disclosure and ESG Ratings.

She holds a Master’s Degree in International Cooperation and Development from the University of Bologna, where she focused particularly on climate change policy.

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Being a developer in a Fintech startup

Upon finishing my Bachelor’s, I never imagined I would find myself in the crossroad between the digital lending market and software engineering, with the former being just a part of the broad Fintech domain. I settled into frontend development with no prior knowledge on the tools that I am using today and although it has been only almost one year since I am developing for CardoAI, I would like to share my thoughts and experience on what it means to be a software developer in a Fintech startup.

You will not be able to understand everything at once

There are a lot of business and financial terms that will take time until you are accustomed to and being a developer, you should start to distinguish between different data visualizations and how to best display this ‘odd’ financial information that you are just starting to learn. However, this is part of the process as you get to immerse yourself in the world of Fintech, so just give it some time because you will not be able to understand everything overnight, even if you possess some previous background on finance and economics.

Ask questions A LOT

Always ask questions. Everyone has distinct learning paths so I do not think there are any wrong questions. Especially when it comes to developing in a rapidly changing market with the latest technologies, you will be judged for not asking questions. I have found pair programming and productive brainstorming sessions for discussing a new requirement to be particularly helpful in sharing knowledge between team members.

Put your customers first

Being part of a startup is a significant experience that provides to the team another mindset on how to approach the product and most importantly: your customers. Customer-Centered Approach is highly emphasized and it is the cornerstone in creating positive and meaningful relationships with your customers. The entire planning process and the development cycle will be adjusted to serve your customers’ needs: what they prioritize and what would give them a competitive advantage in the market. Once you start thinking like the customer and once you embrace the product as your own, you will start to identify problems and even hidden opportunities – leading to proactively improving the software without waiting for a customer request.

Challenges will help you grow

A software developer in Fintech should always expect the unexpected. There will be challenges waiting for you at every corner, but this should not demotivate you; quite the contrary, accept the new challenges coming in your way and use them at your advantage as an opportunity to grow. Having to deal with the look and feel of the application, a new challenge may also help you unravel your inner creative spirit and force you to think out of the box.

Being part of a startup in a Fintech environment means that nothing is certain. The market is extremely volatile and everything is changing rapidly. Although a software developer would not necessarily be interested in the business side of doing things, in this case I think that the involvement of the tech team in better understanding the business requirements is one of the greatest strengths a startup could have. As we recognize this constant change in the Fintech domain, we as developers are one step ahead to deliver highly demanded features and perceive the importance of them by putting ourselves in the shoes of our customers. Flying Airplanes While We Build Them – the catchphrase of CardoAI best summarizes the challenging environment we face.

Nevertheless, as in any industry there will be opportunities to capitalize on and also challenges waiting to overcome. Most of the things, as a developer, will be learned through the ‘hands-on’ approach, however there are gaps that could have been filled if the universities prepared their students better on what to expect upon graduating. In my opinion, it is important that the curricula is updated and adapted around the latest technologies most companies are working with, as well as making general financial and accounting courses compulsory for engineering degrees.

Having said that, newcomers should not be frightened to join, quite the contrary, there are plenty of chances to learn and even with limited knowledge and skills there will always be people willing to help.

Cardo AI top startup at MILAN FINTECH SUMMIT

MILAN FINTECH SUMMIT: A SELECTION OF THE BEST OF ITALIAN AND INTERNATIONAL INNOVATION
Among the over 70 candidates from 18 countries, 10 Italian and 10 international companies were selected based on their potentials on the market

Milan, 23 November 2020 – The Fintech companies deemed as having the highest market potential will be the protagonists of the second day of Milan Fintech Summit, the international event dedicated to the world of Finance Technology, scheduled as a streaming live on 10 and 11 December 2020. It is promoted and organised by Fintech District and Fiera Milano Media – Business International supported by the City of Milan through Milano&Partners, and sponsored by AIFI, Assolombarda, Febaf, ItaliaFintech and VC Hub.

Following the call launched on an international level and a careful selection by such experts in the sectors as the Conference Chair Alessandro Hatami and representatives of the organizing committee, today they announced the 20 companies that will be given the opportunity to be on the digital stage to present their own ideas and solutions for the future of financial services.
Among the over 70 candidates from 18 countries, 10 Italian and 10 International companies were selected.

The Italian companies are: insurtech Neosurance, See Your Box and Lokky; WizKey, Soisy, Cardo AI, Stonize and Faire Labs, operating in the lending and credit sector; Trakti offering cybersecurity solutions; Indigo.ai dealing with artificial intelligence.

The international ones that were selected are: Insurtech Descartes Underwriting and Zelros (France); Keyless Technologies (UK), CYDEF – Cyber Defence Corporation, Tehama (Canada), dealing with DaaS and Cybersecurity and Privasee (UK), operating in the data market protection; Pocketnest (USA), a SaaS company; Wealth Manager Wondeur (France), DarwinAI (USA) operating in the artificial intelligent sector and Oper Credits (Belgium), operating in the lending and credit field.

These realities, which will be introduced to a parterre of selected Italian and International investors and to fintech experts, were chosen based on the criteria of: innovativeness of the proposal, potential size of the target market, scalability of the proposal, potentials in capital raising; type of the employed technological solution.
The Milan Fintech Summit will thus help introduce the potential of our fintech companies abroad reinforcing the role of Milan as European capital of innovation, an ideal starting point for international companies that want to enter the Italian market.

FINTECH ACCESSIBLE TO EVERYONE, AN OPEN DOOR EVENT
The program of the event is available on the official site and a physical appointment of the summit is already scheduled for next year, on 4 and 5 October 2021. The December appointments are open to all those interested in knowing and understanding in depth the potentials of fintech. You can register now for free using this link, or purchase a premium ticket to participate as listeners to the pitch session (the only closed door part of the program) and be entitled to other benefits offered by the Summit partners.

Fintech District
Fintech District is the reference international community for fintech ecosystem in Italy. It acts with the aim of creating the best conditions to help all the stakeholders (start-ups, financial institutions, corporations, professionals, institutions, investors) operate in synergy and find opportunities of local and international growth. The companies that decide to adhere have in common the tendency to innovate and the will to develop collaborations based on opening and sharing, The community now consists in 160 start-ups and 14 corporate members choosing to participate to the creation of open innovation projects by collaborating with fintech. Fintech District also has relationships with equivalent innovation hubs abroad to multiply the opportunity to invest and cooperate, establishing its own role as access door and reference in the Italian market. Created in 2017, the Fintech District has its seat in Milan in Palazzo COPERNICO ISOLA FOR S32, in Via Sassetti 32. Fintech District is part of Fabrick.

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