“We’ve been able to standardize the data, the know-how, and the ways of collaborating amongst ourselves and with our customers so that they can see the work we’re doing, as we do it. Domino accelerates our speed to delivery, providing a much faster and better return on our modeling investment.”
— Jacob Grotta, Managing Director of Risk and Finance Analytics at Moody’s Analytics
Financial services companies analyze sensitive PII and manage money from the public, thus they face strict regulatory requirements. They need full visibility into all project contexts and must be able to reproduce past experiments and model-driven decisions for auditing.
With 24-hour global capital markets and distributed teams, collaboration with colleagues from different teams and regions on both investment ideas and data science models can be difficult, undermining the benefits of collective wisdom.
With the competition for talent from fintech and the broader tech industry, financial institutions need to give quantitative researchers and investment professionals access to the latest tools for contributing to alpha and the bottom line, without sacrificing IT standards for security and compliance.
Capabilities of machine learning algorithms fit nicely with underwriting needs. Quantitative risk analysts can train models to help underwriters work faster and more accurately.
With the help of machine learning, financial institutions can provide personalized products, services, and recommendations without ad hoc manual analyses.
Machine learning can identify anomalies such as unusual patterns in trading data, and alert risk managers to investigate or trigger automatic remediation.
Domino empowers data science and quantitative research teams to be model-driven by streamlining ad hoc experimentation and analysis, accelerating financial model development, deploying and maintaining lineage of credit ratings at scale, rapidly detecting fraud, meeting audit requirements, and more.