Credit and Loan Approvals

AI has proven to increase the funnel of available creditworthy customers by analyzing additional factors beyond credit history. DataForce employs transcription and natural language processing expertise that can help companies analyze and categorize social media posts or website content to increase an engine’s ability to rate a customer’s tendency toward responsibility or risky behavior.

Fraud Detection (AML/KYC)

The use of AI in fraud detection requires sifting through vast amounts of data to identify unusual patterns in customers’ behavior, purchasing habits, and transaction locations. As fraud schemes operate more quickly and become more advanced, financial companies have to keep pace. The global capacity of DataForce annotation and data classification resources helps finance companies develop algorithms that recalibrate and quickly identify data patterns and anomalies that may point to unwanted activity.

Compliance

Regulatory compliance in the financial industry can be improved with the use of AI. DataForce provides immediate input into increasing an AI engine’s ability to identify patterns and extract relevant data from contracts, forms, applications, etc., via labeling. DataForce then verifies the accuracy of the updated model by identifying and correcting mislabeled or unlabeled data, which leads to continuous improvement of your model.

Virtual Assistants

In the financial services space, data shows that customers prefer voice-controlled assistants that are trained to help customers check balances, get stock quotes and rate information, schedule payment reminders, and complete various other tasks. DataForce offers end-to-end virtual assistant support services in more than 200 languages. These services include:

• Voice data collection from diverse users to increase coverage and reduce bias in speech recognition
  Transcription services to increase the accuracy of speech-to-text
• Annotation services to improve the performance of information extraction and data classification systems
• Development of natural language understanding (NLU) and natural language generation (NLG) models
• Phonetic transcription and text-to-speech fine-tuning for a better client experience
• User experience testing