Gartner mentions Altilia in its 1st market guide on Intelligent Document Processing

Altilia recognized at the Milan “Salone dei Pagamenti” event for AI innovations in ESG and Credit Management.

From November 23rd to 25th 2022, Altilia participated in the “Salone dei Pagamenti” event in Milan. Promoted by ABI (Italian Banking Association) in collaboration with Banca d’Italia and Fintech Milano Hub. The event is a national reference point in the payments sector and for innovation in the Italian banking industry.

During the event, the project developed by Altilia, was recognized by Banca d’Italia among the 10 best innovative projects selected as part of the “Call for Proposals 2021” for innovative applications of AI technologies in banking.

The project, made collaboration with the ItaliaFintech Association, the Chiomenti law firm (as legal partner) and with the participation of Prometeia (as partner/user ), is an AI application in the field of ESG investments, aimed at facilitating banks in the process of corporate sustainability risk assessment and corporate sustainability goals assessment.

In particular, the project aims at automating the information gathering processes from different sources and documents (such as financial statements, sustainability reports and corporate websites) and, leveraging advanced Document Understanding and Natural Language Processing (NLP), provide automatic text comprehension and classification to create highly detailed ESG company profiles. These profiles can then be used by banks to monitor the evolution of ESG goals and risks over time and integrate the information to enhance their lending application procedures.

With the Altilia Intelligent Automation platform, users can directly train AI models to recognize the target data of interest. Using a no-code interface users can create a set of annotated document examples, and, with a sufficient number of examples, the algorithm becomes autonomous in recognizing the target data. The final results can be either stored in the platform or exported to be leveraged by other applications like CRM systems or customer’s databases. Thanks to this connectivity it is possible to automate the creation of detailed ESG report and risk scores.

With the proposed solutions banks can obtain up to an 80% reduction of the manual work required for ESG profiling, uplifting employees from all the repetitive tasks related to document reading and information extraction, and accelerating ESG profile generation speed by up to 10 times. In addition, the resulting profiles are more detailed and complete, with an accuracy score (F-score) of 95%.

At the closure of the “Salone dei Pagamenti” event, the project has been presented to Bank of Italy’s Governor Ignazio Visco, who directly met the awarded teams, and congratulated with Massimo Ruffolo, founder and CEO and of Altilia.

If you want to learn more, here you can find more information about AI applications for ESG in credit management.

How to facilitate the adoption of Large Language Models in pharma and finance

After the initial enthusiasm about Large Language Models (LLMs) due to their impressive results that made them SOTA approaches for some new and traditional general purposes NLP tasks (e.g. question answering, text classification, information retrieval, token sequence tagging, entity extraction, sentiment analysis, intent detection, word sense disambiguation, POS-tagging), now it’s the time of reality.

Peraphs, LLMs are contributing more than computervision to the new AI spring. LLMs are powerful tools that can create great value in industry and help organizations to streamline business processes, improve operations, save a lot of money, and improve business performances. But, as a recent neural article optimally points out, they must be carefully adopted, furthemore they need to be fine tuned to solve specific business problems in vertical domains.

For this reason in Altilia we have built a platform that helps users to fine tune LLMs and control their results by human-in-the-loop AI, augmented intelligence, and composite and adaptive AI tools.

Users in highly specialized areas like pharma, health, and finance have to face problems cited in the article. In particular, LLMs cannot be used as they are. These models  are trained over general available data, hence specific data programming and fine-tuning techniques are needed to make them working in a trustworthy, explainable way on highly specific, regulated, privacy and security sensitive tasks.

Altilia Intelligent Automation platform leverages all LLMs available in huggingface and provides users with tools that allow to curate and program data set to fine-tune LLMs and apply them to solve any NLP and document processing task at the highest level of accuracy and trustworthiness needed by the users operating in highly regulated environments.

For more information read the following use cases: improving NPL data tapes management; enhancing credit scoring for lending applications; Extract ESG data from multiple sources; data extraction from notes to balance sheets.

Altilia nominated as Top Digital Banking Solutions Provider in Europe 2022 by the Financial Services Review

The Financial Services Review magazine, a renowned digital and print publication with a solid reputation in discovering innovative technology trends and solutions for the Financial Services Industry, has just awarded Altilia as a Top Digital Banking Solutions Provider in Europe 2022.

Altilia has been nominated for its efforts to develop innovative solutions to automate many activities in the financial and banking sector, involving complex document intensive processes that still require manual work.

One relevant example is corporate ESG (Environmental Societal Governance) profiling for credit scoring purposes. In this case, Altilia is capable of classifying and extract target data from different types of documents (like balance sheets, annual reports, and financial statements), contained in several different sources (including corporate ECM systems).

Output data can then be used for the credit scoring algorithms to compute interest rates for loan applications. With this method, Altilia has been able to cut down manual work time requirements for the task by around 80%, with the effect of drastically reducing the loan processing lead time from ten days to just 2 days, thus greatly improving customer satisfaction.