Financial Markets

Financial Markets firms are at a unique place and time in their evolution. They are faced with some of the biggest challenges in their history: full electronification of markets, technology disruption, fee compression, and lower client switching costs, to name just a few. At the same time, they are embracing some of their biggest opportunities, many made possible by technology: the convergence of data ubiquity, high-speed processing, and advanced technologies, such as AI and machine learning.

Discover how automated machine learning is driving the Financial Markets revolution.

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AI and Financial Markets

As financial markets firms work to digitize and transform for growth and operational efficiencies, they are aggressively innovating and differentiating, as they compete for share of assets and create a next generation client experience. AI helps firms to reimagine their operations by accelerating these initiatives and by leveraging their own data and delivering bottom-line results that help them not just compete but win.

Exceeding Client Expectations

Identify client churn risk
Retain the highest lifetime value clients
Anticipate appropriate product offers (cross-sell/upsell strategies)
Optimize new customer acquisition strategies
Deepen client relationships
Increase share of wallet

Risk Exposure

Predict risk for ratings and outlooks
Identify suspicious anti-money laundering activity with fewer false positives
Detect and prevent account takeover and wire fraud
Monitor trading and communication
Detect and prevent cyber threats

Improving Operational Efficiency

Understand channel effectiveness
Predict customer profitability
Asset allocation modeling
Predict fund net inflow
Reduce middle and back-office cost from process failures and error correction
Optimize trade execution and routing for trading cost optimization

Exceeding Client Expectations
- Identify client churn risk
- Retain the highest lifetime value clients
- Anticipate appropriate product offers (cross-sell/upsell strategies)
- Optimize new customer acquisition strategies
- Deepen client relationships
- Increase share of wallet
Risk Exposure
- Predict risk for ratings and outlooks
- Identify suspicious anti-money laundering activity with fewer false positives
- Detect and prevent account takeover and wire fraud
- Monitor trading and communication
- Detect and prevent cyber threats
Improving Operational Efficiency
- Understand channel effectiveness
- Predict customer profitability
- Asset allocation modeling
- Predict fund net inflow
- Reduce middle and back-office cost from process failures and error correction
- Optimize trade execution and routing for trading cost optimization

Use Cases Across Financial Markets

The opportunity to apply AI and machine learning across financial markets is expansive. There are thousands of applications for automated machine learning to help ensure that financial markets firms are making faster and better decisions.

Asset Management

Optimizing your portfolio is critical to your firm’s success. By deploying the right predictive models, fund managers can anticipate changes in cash position impacting liquidity. As these models improve over time, your portfolio’s performance becomes a differentiator.
Marketing and Cross-Selling

Customized marketing campaigns increase ROI and improve response rates. Automated machine learning models identify the clients most likely to respond, along with the best offers and the best prospects. Deliver higher success rates with new customer acquisition and cross-sell and upsell programs.
Fraud and Financial Crimes

Fighting financial crime by identifying suspicious activity is critical, especially as criminals get more sophisticated. It also enables investigations to take place before loss occurs. Using machine learning, your firm can learn from investigational findings and incurred losses to accurately detect activities like account takeovers or wire fraud, while maintaining effective trade and communication surveillance.
Attrition Management

Clients expect their firm to provide superior customer service. With automated machine learning, firms can identify clients at high risk of attrition by learning from examples of clients that have closed or moved accounts in the past. Preemptive client engagement can uncover issues that are driving both dissatisfaction and churn.
Trading Operations

Traders are using historical transaction cost analysis (TCA) and execution data to build models that optimize order routing and trade execution strategy. These models evaluate the relative merits of the numerous potential algorithmic trading approaches, venues, and counterparties. In addition, these support trader decision-making and help to minimize market impact and cost, while demonstrating and recording the trader’s compliance with regulatory requirements.

DataRobot Can Help:

Business Line and Functional Heads

Even if you do not have deep data science talent on your team, you can leverage AI to tap into the data that your firm already has. Data and business analysts without formal data science training can be enabled to build and deploy sophisticated AI models to drive business value.
Chief Data Officers

Automated machine learning helps you optimize the productivity of your existing data science team. With DataRobot’s automated model risk management and model validation templates, you can relieve data scientists from documentation requirements. The DataRobot platform gives you simple deployment options that can help you get solutions to market faster by finding the best models for your organization.
Data Scientists

You probably have a backlog of analytics requests. DataRobot can help. By taking on low-risk models from start to finish, the DataRobot platform can free up your time to focus on projects where the payoff is the greatest. Using automated machine learning to build many models at once saves you the time and effort of testing and comparing every model — increasing precision and giving you more model granularity.
Chief Information Officers

Most firms have plenty of data but not enough analytics staff to turn that data into insight. With DataRobot, you can democratize data science and watch the performance of your business take off, as the data reveals opportunities and improvements that can help you to monetize your investments in data infrastructure.
Chief Technology Officers

Using DataRobot’s low-risk model deployment options, such as code generation, deployment to Spark, and API-based deployment capability, you can bring AI and machine learning-based solutions to market faster.

DataRobot justifies its place by providing value and returning significant ROI immediately.

Beaumont Vance
Head of Enterprise Analytics, TD Ameritrade