

Customers' Business Cases
La préparation des données avec Tale of Data est présente chez de nombreux clients, tous secteurs d'activité.
Vous trouverez ci-dessous quelques exemples de clients qui utilisent notre solution de préparation de données pour mener à bien leurs projets de fiabilisation de données, d'analyse de données et d'enrichissement de la donnée.




Nos clients
Détection de fraude
Blanchiment
Fraude à la TVA
Fausses factures
Financements occultes




Industrie
Données de capteurs (IoT)
Transactions financières
Plant Information
Détection d’anomalies
Assurances
Fausses déclarations
Déshérence
Evaluation des risques
Solvabilité II (pilier 3)
Nos clients en détails.....

Détection de fraude
Blanchiment
Fraude à la TVA
Fausses factures
Financements occultes
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Détection de la fraude aux documents administratifs
Detection of document fraud
Our client, a French ministry, wanted to improve the efficiency of controls over the allocation of administrative documents.
The size of the database (nearly one hundred million records) and the variety of applications allowing the entry of information - most often manual entry - severely limited the effectiveness of fraud detection.

Marketing
Optimization of Marketing Campaigns by improving Data Quality and enriching CRM data
Our client wanted to increase the relevance of the marketing messages sent to his customers. To achieve this goal, he needed to improve the segmentation of its customer base and therefore solve the following two problems:
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Reliability of CRM data: multiple views of the same customer (duplicates), inconsistencies in emails, postal addresses and phone numbers
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The lack of contextual information about customers in the CRM

Migration Intégration
Segmentation
Migration CRM
Recommandations
Optimisation de campagnes marketing
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Banque et finance
Risque de Crédit (RWA)
BCBS-239
KYC
Bâle III
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Banque et finance
BCBS 239 - Compliance
Our client, one of the most important private banking players in Europe, had an obligation to comply with the BCBS 239 standard.
On January 9, 2013, the Basel Committee published a set of principles under the name BCBS 239, the objective of which was to enable banks to increase their reporting capacity and the accuracy of regulatory reports.

Partage des données
Aggregating Multiple Databases with Record Lineage
Our client wanted to publish, on a single portal, a database resulting from the pooling of records fetched from 12 source databases.
Since overlaps existed between the different source databases, it was necessary to deduplicate so that portal visitors had a single view of each record.
Additionally, since the users of the portal were able to correct and / or enrich the data (= Crowdsourcing), it was necessary to keep, for each entry in the aggregated database, a link to the corresponding record(s) in the source databases (= Record Lineage), in order to pass on the record-corrections to the source.

Partage des données
Open Data
Intranet
Réutilisation
Travail collaboratif
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Assurances
Fausses déclarations
Déshérence
Evaluation des risques
Solvabilité II (pilier 3)
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Marketing
Notre client souhaitait améliorer l’efficacité des contrôles sur les comptes inactifs ou les contrats d'assurance vie en déshérence.
La solution Tale of Data lui permet d’identifier de manière unique ses clients :
- Rapprochement des personnes physiques ou morales fortement similaires grâce au moteur de dédoublonnage multicritères, multi-algorithmes
- Détermination d’un score de similarité permettant un dédoublonnage fin
- Enrichissement avec des référentiels externes (référentiels métiers, Open Data, …)

Sécurité
Internal threats: prevention of the leak of sensitive information
Our client, one of the largest private banking players in Europe, wanted to minimize the risk of sensitive information leaking (identities, financial transactions, etc.). Since this type of leak is most often due to internal malicious acts, the Information Systems Security Manager wanted to exhaustively identify the sensitive information present in the bank’s information system in order to increase the level of protection.
Two questions therefore arose:
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Where exactly are stored sensitive data? Which databases? What tables? Which columns? But also which files? (e.g. Excel files and other listings disseminated on the internal network)
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What types of sensitive data are these?

Sécurité
Lutte contre le financement du terrorisme
Cyber-sécurité
Menaces internes
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Compliance et risques
Audit
Gestion des Risques
Litiges
RGPD
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Compliance et risques
Personal data scans for GDPR compliance
Our client had to comply with the General Data Protection Regulation (GDPR). In order to do that, all the personal data present in his information system had to be associated with processing acceptable to the supervisory authority.
To achieve this goal, our client had to be able to answer these 4 questions:
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Who within the company keeps personal data?
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What types of personal data are these?
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Where are these personal data stored? Databases but also Shadow IT (e.g. Excel files disseminated on the internal network)
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For what purpose are these data kept?

Industrie
Monitoring and improving the quality of PI data
Our client, an industrial group with hundreds of subsidiaries around the world, wanted to control and improve the quality of PI data (PI = Plant Information: data emitted by sensors installed on production sites).
The objectives were as follows:
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Create PI nomenclatures (Assets, Attributes, Tags) with clear naming rules, which are free from duplicates in order to allow better reuse of Tags as well as cross-site analyzes.
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Set up an efficient monitoring system for PI Tags (= time series): real-time detection of missing or inconsistent data, identification of defective sensors, etc.
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Supply Data Scientist teams with reliable data, which is an essential prerequisite for building consistent and efficient predictive models (forecasting, predictive maintenance, etc.).

Industrie
Données de capteurs (IoT)
Transactions financières
Plant Information
Détection d’anomalies
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