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Transforming Compliance with Workflow Automation: A Game-Changer for Banks & Financial Institutions In the rapidly evolving financial sector, banks and financial institutions face increasing pressure to enhance compliance, reduce fraud risks, and streamline operations. Manual compliance workflows often result in inefficiencies, delays, and regulatory risks. This is where workflow automation powered by tools like Camunda can revolutionize the way financial organizations handle processes like KYC onboarding, AML compliance, and audit workflows. The Challenges in Compliance & KYC Onboarding Traditional compliance and KYC (Know Your Customer) processes are often plagued by: How Our Workflow Automation Service Helps We provide custom workflow automation solutions tailored for banks and financial institutions. Our service ensures: Seamless KYC Onboarding & Customer Verification Automated Compliance & Audit Workflows Fraud Detection & Risk Assessment Why Choose Us? Get Started Today! Let’s transform your compliance process with workflow automation. Contact us today for a free demo and see how we can help your bank stay ahead in the digital era!

Leveraging LLMs to Streamline Pharma Case Processing: Reducing Time and Costs with iRxSafe Highlights: iRxSafe is a AI driven Pharmacovigilance intake case processing platform Handles incoming emails and classifies them as safety, product quality issue or other categories AI based extraction of drug details, adverse event details, patient conditions etc (Based on trained & fine tuned LLM Claude Sonnet) Automatic follow up email writing to collect missing details to enable the case to be processed The solution is end to end hosted inside AWS, ensuring data security and scalability and can be plugged into a larger case processing & reporting system (As an intake handling tool) How LLMs are used: The trained LLMs are used for the following tasks. Identifying the category of the incoming email – into the following categories Product safety (adverse event) Product quality Seeking product info Spam or other categories Extracting details of the adverse event / case Primary drug and dosage Patient information Adverse event information Concomitant drugs if any Patient conditions for which the drug was taken , etc Creating Followup email for requesting missing information We have evaluated various models and we have zeroed on Claude Sonnet for its better performance of the above tasks cost effectively. We had to fine tune the model for certain conditions of identifying primary drug and concomitant drugs and the model could improve the performance after the training. Human in the loop was built into the system where the System waits for user confirmation of the AI models’ conclusions, where the user can edit, correct and add information which the AI models fail to detect. This information again fed into the training pipeline for further fine tuning the model.