PAR SECTEUR

NLP

Le traitement du langage naturel (Natural Language Processing - NLP) permet aux machines de comprendre, d'interpréter et de répondre au langage humain.

NLP - de quoi s’agit-il ?

Le traitement du langage naturel (NLP) est une branche dynamique de l'Intelligence Artificielle (IA) qui permet aux ordinateurs de comprendre, d'interpréter et de répondre au langage humain. Il s'agit d'une technologie qui couvre les données structurées et non structurées, gérant non seulement des ensembles de données traditionnels bien organisés, mais également diverses formes d'informations non structurées telles que les e-mails, les vidéos et les présentations PowerPoint. En exploitant le NLP, les machines peuvent extraire des informations significatives de ces différents types de données. Par exemple, le NLP peut analyser les mots prononcés dans une présentation vidéo (données non structurées), les convertir en texte et les traiter ultérieurement pour extraire des informations ou des résumés, créant ainsi une sortie structurée à partir d'une entrée non structurée.

Quels sont les avantages du NLP?

Le traitement du langage naturel (NLP) offre une myriade d'avantages, rendant les interactions informatiques plus humaines et plus compréhensibles.

Quand parle-t-on de RPA?

Intelligent Process Automation (IPA) is an advanced form of technology that orchestrates the creation, deployment, and management of smart bots designed to mimic human activities within digital interfaces. These bots can astutely perform tasks such as extracting customer information from a database and using it to generate invoices. They are capable of interpreting on-screen data, executing accurate keystrokes, seamlessly navigating through diverse systems, and extracting relevant data. An added layer of sophistication allows these bots to learn from previous tasks, adapt to changes in process, and even handle unstructured data, proving their robustness in handling a vast array of complex digital tasks.

Quels sont les avantages du RPA?

Robotic Process Automation (RPA) presents a revolutionary approach to streamlining business operations, offering significant advantages ranging from enhanced efficiency to improved accuracy.

NLP Process Examples

Service client amélioré

Le traitement du langage naturel (NLP) aide à comprendre les sentiments des clients pour des réponses personnalisées.

Où peut-on utiliser NLP ?

Le traitement du langage naturel (NLP) remodèle aujourd'hui divers secteurs et lieux de travail. Dans le secteur de la santé, il est utilisé pour extraire des informations de dossiers de patients non structurés, améliorant ainsi la prestation de soins. Le secteur financier utilise le NLP pour l'analyse des sentiments afin de prédire les tendances du marché. Dans le service client, le NLP est utilisé pour améliorer les interactions entre êtres humains et chatbots. Au sein de l'administration fiscale, le NLP permet de traiter et d'analyser une multitude de déclarations et de documents fiscaux pour plus de précision et de détection des fraudes. L'industrie hôtelière l'utilise pour comprendre et répondre aux commentaires et aux avis des clients. Enfin, dans le secteur technologique, il alimente les assistants vocaux et les moteurs de recherche, révolutionnant l’interaction homme-machine.

Check out in depth what Automation can deliver for Banks in these cases:

Risk and compliance assessments.

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Yet another
case

These essential components of banking and financial operations, aiming to ensure that banks operate within legal and regulatory requirements and manage various forms of risks.

Comment AI make RPA better?

Robotic Process Automation (RPA) greatly benefits from advancements in Artificial Intelligence (AI). While RPA is excellent at following rules-based processes, AI provides the capability for RPA bots to learn, adapt, and make decisions, enhancing their functionality. AI-powered cognitive capabilities like Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision enable bots to understand and respond to text or voice commands, learn from historical data, and recognize images, respectively. For instance, in customer service, AI-powered RPA can analyze customer sentiments in real-time, allowing bots to handle customer complaints and queries more effectively, delivering personalized responses and improving overall customer experience.