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Capitalize on the Power of Value-Driven AI in Financial Services

In a technological revolution reshaping financial services, don’t settle for outdated “industry standards” and embrace transformative generative and predictive AI solutions.

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DataRobot allows financial services companies to automate the painful parts of the machine learning lifecycle, giving data science teams the time and tools to focus on business impact. Our clients are able to better understand, govern, and evaluate both predictive and generative AI models, find investment opportunities quickly, expand their portfolios, and nimbly respond to rapidly changing market conditions.

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Propensity to Buy: Offering Financial Products Through Value-Driven AI

Learn how financial institutions enhance their production maturity and utilize their data to deliver better business outcomes.

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Financial Service Organizations Unlock Real Value with AI

20 to 40
basis point ROA increase
Potential yield from significant reduction in time to market for AI and ML
Source: McKinsey & Co.
31%
Increase in model inventory sizes of US banks since 2021
Source: McKinsey & Co.
$1 trillion
Potential annual value of AI and analytics for global banking
Source: McKinsey & Co.

AI Is Solving a Variety of Challenges in Financial Services

Maximize compatibility with
tools in place

Use predictive and generative AI in your existing tech ecosystem in FinServ or FinMarkets.

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Uncover new revenue opportunities

Quickly find the signal in the noise and execute on new customer and investment opportunities.

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Reduce your risk profile

Maintain visibility, control, and governance across all your predictive and generative AI projects.

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Keep customers happy

Improve customer retention through better targeting and customer experience.

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Reduce costs

Do more with less by increasing the productivity of your existing staff.

Trusted by 8 Out of Top 10 US Banks, 4 of the Top 10 Global Banks

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Value-Driven AI for Financial Services and Financial Markets

Accelerate and de-risk becoming an AI-first financial institution.

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Consumer Banking

Increase your efficiency throughout the customer journey. Speed up throughput for credit decisions; target consumer banking customers more effectively and efficiently. Decrease risk with a holistic, real-time view of customer activity, enabling you to take action as conditions change.

  • Mortgage Banking

    Make quick credit decisions while also reducing consumer lending risk. Use generative AI to quickly search and conform to fairness in lending and other regulatory requirements.

    Use cases:

    • Prepayment risk
    • Delinquency risk
  • Sales, Marketing, and Relationship Management

    Increase revenue and efficiency with tailored marketing, qualification, and prioritization of leads, increasing conversion and deepening relationships with existing customers. Use generative AI to analyze prior customer actions and automatically suggest new products appropriate for those customers.

    Use cases:

    • Targeted marketing
    • Lead qualification / optimization
    • Profitability prediction
  • Retail Banking

    Use predictive analytics to analyze emerging liquidity problems in real time.

    • Predicting deposit inflows and outflows
    • Scenario analysis with rising interest rates

    Use data to price products more accurately, adjusting for risk.

    • Deposit potential / stability
    • Risk-adjusted pricing and price elasticity
    • Collections optimization
  • Risk and Compliance

    Reduce the manual work needed for predictive modeling to uncover fraudulent activities across millions of transactions. Identify and isolate security threats before they become problems.

    Use cases:

    • Real-time fraud detection and prevention
    • Suspicious activity monitoring
    • Anomaly detection
    • Cyber threat detection and attack recognition

Commercial Banking

Use your historical data to your advantage, using all of the data in your ecosystem to drive more accurate forecasting. Use granular signals to enhance the corporate customer experience, with automated alerts (and quick responses) when conditions change.

  • Liquidity and Treasury

    Forecast sales and opportunity conversion more accurately for cash flow and expense controls. As economic conditions tighten, detect changes quickly and make actionable budgeting decisions.

    Use cases:

    • Cash flow projections/forecasting
    • Relationship profitability prediction
    • Loss forecasting
  • Commercial Lending

    Increase your credit decision throughput through automation without increasing your staff.

    Decrease your risk in lending with a data-driven approach that supplies more accurate pricing.

    Use cases:

    • Small business, commercial credit, and collateral valuation
    • Credit scoring and approval
    • Credit pricing
  • Sales, Marketing, and Relationship Management

    Diversify your deposit mix quickly and efficiently, using data to qualify prospects and cross-sell opportunities.

    Holistically view your depositors’ activity, detecting granular signals of potential deposit volatility. Use generative AI to create smarter automated chats to solve customer problems and enhance selling opportunities.

    Use cases:

    • Prospecting
    • Relationship deepening
    • Pricing analytics
    • Deposit volatility
  • Risk and Compliance

    Reduce the number of false positive alerts for fraud and failed processes.

    As economic conditions change, monitor your model fit over time to ensure it’s still performing (and not introducing additional risk). Utilize guardrails around generative AI models to ensure their accuracy.

    Use cases:

    • Operational risk, process fails, false positives reduction
    • Targeted risk review
    • Fraud detection and prevention
    • Predicting risk adjusted return (RAR)
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Financial Markets

Increase your productivity by outsourcing machine learning knowledge and focus on understanding financial markets data.  Automate building model factories and carry out quant research at a higher level of abstraction by searching across problem spaces rather than algorithm choices or model parameters. Decrease painful manual work, while decreasing risk.

  • Trading

    Quickly build more efficient trading models and systems. Experiment and iterate fast by cutting down setup time for quant research projects. Utilize generative AI to suggest macroeconomic features for forecasting and trading use cases.

    Use cases:

    • Price discovery
    • RFQ price elasticity
    • VWAP curve forecasting
    • Fast complex exotic derivatives pricing
  • Investment Banking

    Use AI to scale your businesses and grow coverage by quickly identifying undervalued companies and acquisition targets. Improve your targeting of institutional investors, using generative AI to create lists of those who are most likely to invest in specific deals.

    Use cases:

    • Event prediction
    • Investor targeting
    • Issuer targeting
  • Operations/Compliance

    Cut your manual work by applying “smart alert” processing to reduce trade breaks and payment fails, while improving customer experience.

    Automate and accelerate your risk management, reducing your operational risk profile.

    Use cases:

    • Trade failures
    • Anti-money laundering
    • Data quality
  • Investment Research

    Leverage automated machine learning techniques in the investment process to model, explain, and predict variables that influence stock valuation. Use generative AI to suggest additional variables that could influence earnings.

    Use cases:

    • Earnings surprise prediction
    • Asset class return potential
    • Model risk reduction

Take AI From Vision to Value

See how a value-driven approach to AI can accelerate time to impact.