Emerging technologies and collaborative workflows add complexity to protecting unstructured data:
Unstructured data in Financial Services
Financial services firms leverage advanced analytics, artificial intelligence (AI), and machine learning (ML) techniques to extract insights and value from unstructured data. By analyzing both structured and unstructured data, they can enhance risk management, improve customer service, identify potential fraud, and gain competitive market advantage.
Examples of unstructured data used in financial services include text, including email, social media posts, customer reviews, research reports, and legal documents. Audio data might include customer service calls, meetings, conference, and speech-to-text technologies. Video includes recorded webinars, training sessions, surveillance footage, and video conferencing data. Photographs and scanned document images are used for identity verification, document processing, and fraud detection. And unstructured market data might include unformatted financial reports, news articles, and analyst notes.
Unstructured data typically lacks organizational. Without clear labels, metadata, or defined data fields, it can be difficult to classify and even more challenging to identify sensitive data requiring protection.
Comprising a multitude of formats, including Word documents, PDFs, PowerPoint files, images, audio files, and multiple video formats, creating a consistent and effective security strategy can be a challenge.
With unstructured data being generated by a multitude of sources – including social media, emails, documents, images, and videos – the sheer volume and rate of growth requires the ability to easily manage across different storage types and regions, adding to the challenge of identifying and protecting sensitive information, along with maintaining regulatory compliance.
Unstructured data is frequently shared among different stakeholders – including medical professionals, insurers, patients, even artificial intelligence – creating yet more unstructured data – making it difficult to restrict access to sensitive information and prevent data leakage.
Unstructured data is often stored across several locations – from shared drives and mobile devices to cloud storage across multiple regions – making it difficult to keep track of and adequately secure.