Banking

AI & ML solutions

The banking sector is one of the leading adopters of Machine Learning technologies. Banks and financial institutions are prioritizing investments in Artificial Intelligence to better serve their customers, conform to regulations, tackle fraud or money laundering, and reduce risks.

NATURAL LANGUAGE PROCESSING - Content Moderation

Enterprise Correspondence Monitoring

Banks employ stringent data security laws to prevent breach of sensitive information. Misaddressed or unauthorized Emails / messages may lead to data loss or security threats. Using Natural Language Processing, AI systems can monitor and identify suspicious correspondence content. Messages having highly secretive data or recipients not suitable for their context are automatically flagged and escalated to the Compliance/Data Security personnel. Using this, AI helps Banks reduce costs from fines of data security laws infringement.

Enterprise Correspondence Monitoring

NATURAL LANGUAGE PROCESSING - Content Moderation

Regulatory Compliance Monitoring

Regulatory changes have increased exponentially for the financial industry in the past decade. Compliance officers have to interpret tons of regulatory documentation manually and run the risk of making mistakes and oversights. Employing AI, banks can automatically curate regulatory content from financial, federal and state-level regulatory sources. Using Named Entity Recognition (NER), meaningful insight can be extracted quickly from this content, saving banks time and reducing manual resource costs. Banks can then align their policies and workflows to meet these regulations much more efficiently.

Regulatory Compliance Monitoring

NATURAL LANGUAGE PROCESSING - Named Entity Recognition

Financial Contract Analysis

Documentation analysis is one of the most time-consuming, yet most crucial processes in financial institutions. Employees spend a lot of time reading through physical and digital documents, and can still miss key information. Using Named Entity and Optical Character Recognition, AI models can assist in this analysis by automatically extracting relevant clauses and entities from Loan/Credit Agreements, Collateral Valuations Reports, Financial Leasing Contracts etc. This saves Banks hundreds of thousands in man hours, minimizes risk, uncovers hidden costs and maximizes revenue by diverting employee attention to more productive tasks.

Financial Contract Analysis

NATURAL LANGUAGE PROCESSING - Named Entity Recognition

Bank Transaction Screening

All bank transactions are screened to check if the entities involved are on a blacklist. Legacy filtering systems produce a high rate of false positives that then must be further escalated for review. Using Named Entity Recognition (NER), AI can identify entities that are parties to suspect transactions. Extracting party details from transactional text information, ML models can review transactions faster and with more accuracy. By implementing AI, banks can massively reduce regulatory costs due to non-compliance fines.

Bank Transaction Screening

More Case Studies and Blogs

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Computer Vision in Artificial Intelligence

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KYC Automation In Banks Using Computer Vision

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Fraud Detection in Banks Using AI and Machine Learning

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