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  • implement-ai-to-mitigate-risk-header.png

    Implement AI To Mitigate Risk

    Financial Services

    SoftServe automated adverse media screening for one of the world’s largest custodian banks to save 50% on reputation protection

    Your bank’s reputation is inextricably linked to the people and institutions with whom you do business. Stay updated on negative news about business partners that could affect your reputation or regulatory compliance.

    Adverse media screening (AMS) scours news articles and public databases, including court records and government watchlists, to mitigate risk and avoid reputational damage. However, manual searches are time-consuming and costly. AI/ML solutions make protecting your bank’s reputation more efficient.

    Keep up to date on your partners

    implement-ai-to-mitigate-risk-image.png

    Our client is one of the largest custodian banks in the world. As a U.S.-based global investment bank, it deals with numerous clients and partners in diverse locations with unique regulatory guidelines. The bank needs to be aware of news concerning its customers that could risk compliance or damage its reputation.

    AMS is expensive and inefficient. It requires the manual labor of 30 full-time employees. The bank needed to optimize the process to reduce costs and optimize risk management.

    Aggregate and evaluate negative news

    Digitizing and automating manual AMS searches were necessary to optimize reputation protection. The bank needed a platform to:

    • Empirically score and assess negative news 
    • Store negative news searches and metadata 
    • Conduct global and scalable searches in the cloud 

    Implement AI to mitigate risk

    implement-ai-to-mitigate-risk-image-1.png

    SoftServe and the client leveraged the power of Google Cloud and AI to enhance searches. Working together, they:  

    • Implemented Google search REST API to automate client onboarding and periodic checks for negative news. This filled information gaps based on the previous binary search.
    • Deployed natural language processing (NLP) solutions to extract, classify, and score keywords and analyze search results. It also enabled a negative search and sentiment analysis of the information gathered.

    The solution reduced the amount of manual effort required for AMS to increase efficiency and reduce operational costs. The 12-month project led to greater than 50% savings in reputation protection efforts.

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