Secure Automated Translation: an essential tool for data governance in accordance with Basel & Solvency requirements

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Secure Automated Translation

Following the publication of the Basel (II and III) and Solvency Regulations, implementing Governance, Risk and Compliance (GRC) practices within financial institutions has gained predominance. To meet the challenges of data governance stated in the pillars of these regulations, companies have multiplied their efforts to recruit the best in GRC concerning Risk Management, Internal control, Internal Audit and Compliance.

According to a study conducted by the consulting company Optimind Winter and mandated by the Observatoire des métiers de la Banque (Banking Career Observatory)[1], “Banking companies are also victims of new risks and must create new job profiles to deal with these new challenges. GRC departments must face new challenges, such as coverage of systemic risk, development of Cloud Computing or Mobile Bank (technological nomadism).” Here, we refer to digitalizing the banking sector and using the Internet to manage personal bank accounts. Banks and insurance companies have thus implemented solutions to protect their customers’ data against cyber-attacks. While the efforts made to secure customer data have proven effective, the threat now lies within financial companies in their daily workflow.

Along with new working methods, a new phenomenon has emerged, known as “Shadow IT,” which is any application or method of transmitting information used in a business process without the endorsement of the internal IS department. Often unaware of its existence, IT departments don’t provide any support. Such processes generate "informal" and non-controlled data that can contravene existing standards and regulations such as Basel and Solvency. Continue reading

La traduction automatique sécurisée : un outil essentiel pour une gouvernance des données conforme aux réglementations Bâle & Solvabilité

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Traduction automatique sécurisée

La mise œuvre de pratiques industrialisées de « Gouvernance, Risques et Conformité » (GRC) au sein des institutions financières, a pris une place prépondérante depuis l’implémentation des réglementations Bâle (II puis III) et Solvabilité. Pour répondre à l’enjeu de bonne gouvernance des données, stipulé dans les piliers de ces réglementations, les entreprises multiplient les efforts pour recruter les meilleurs dans le domaine de la GRC – Gestion des Risques au sens large, du Contrôle Interne, de l’Audit Interne et de la Conformité.

Selon une étude de l’Observatoire des métiers de la Banque réalisée par le cabinet de conseil en gestion des risques Optimind Winter[i], « […] Les banques sont également victimes de nouveaux risques et doivent se doter de nouveaux métiers afin de maîtriser ces nouveaux enjeux. Les métiers du Risque et du Contrôle dans la banque doivent faire face à de nouvelles problématiques, telles que la couverture du risque systémique, le développement du Cloud computing ou de la banque sur mobile (« le nomadisme technologique»)… ». Il est ici fait référence à la digitalisation du secteur bancaire et l’utilisation de plus en plus courante d’Internet pour gérer ses comptes en tant que particulier. A ce titre, les banques et assurances se sont prémunies d’outils pour protéger les données de leurs clients contre les cyberattaques. Si les efforts réalisés en matière de sécurisation des données clients sont avérés, la menace réside à présent au sein même des entreprises financières, dans leurs flux de travail quotidien. Continue reading

Translate within extended enterprises. What does that mean? – Part I


AFNET, a non-profit society for boosting the digital transformation within vertical industries

AFNET is a French non-profit organization that promotes best practices for processes within the extended enterprises. This society introduces Internet in France in 1992 and was the owner for delivering Internet access to the very first users, mainly in universities.

Since this early age, AFNET has been continuing to promote good practices for enterprises within their ecosystem of suppliers, partners and key customers. These IT practices consist on standards, a common information system, and a partner framework for co-working in confidence.

Today, the best approach consists to leverage industry verticals to build open systems for digital transaction, content collaboration and product design.

Corporations are grouped within industry verticals and are highly dependent themselves. Just a few examples: Do you know that less than 20% of Dassault Rafale airplane parts are done by Dassault Aviation itself? Do you know that behind an Airbus air plane, there is a network of more than 1,200 different suppliers structured by more than 5 depth levels?

To be competitive and efficient within our globalized world, the European aerospace industry had organized itself with AFNET expert assistance to create a common information system named BoostAeroSpace: AirSupply as the platform for managing the supply chain, AirCollab as the platform for data communication exchange and Airdesign as the platform for product design. All these highly-secured platforms managed by the nonprofit organization and financed by all aerospace members make a strong asset that contributes to Airbus success.

Similar projects are in progress today for automotive, energy, travels, that leverage aerospace good practices. All of them share the common vision of the extended enterprise. Continue reading

Voyage en traduction automatique

« Mais enfin, papa, tu ne vas pas aller là ! La traduction automatique, ça marche pas ! c’est pourri …», ainsi s’exprime la fille de l’auteur, 19 ans. Trait générationnel, elle est spontanée, directe.
L’auteur se gratte le front, légèrement ébranlé. Dans la main, il tient sa proposition d’embauche chez le leader mondial de la « machine translation ».

« Mais pourquoi dis-tu ça ? » Continue reading

How does Neural Machine Translation work?

The representation of meaning in Neural, Rule-Based and Phrase-Based Machine Translation

In this issue of step-by-step articles, we explain how neural machine translation (NMT) works and compare it with existing technologies: rule-based engines (RBMT) and phrase-based engines (PBMT, the most popular being Statistical Machine Translation – SMT).

The results obtained from Neural Machine Translation are amazing, in particular, the neural network’s paraphrasing. It almost seems as if the neural network really “understands” the sentence to translate. In this first article, we are interested in “meaning,” that which gives an idea of the type of semantic knowledge the neural networks use to translate.

Let us start with a glimpse of how the 3 technologies work, the different steps of each translation process and the resources that each technology uses to translate. Then we will take a look at a few examples and compare what each technology must do to translate them correctly.

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