Financial Markets

Financial Markets are at a unique place and time in their evolution. They are faced with some of the biggest challenges in their history: full digitization of markets, technology-driven 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 the advanced technologies that comprise enterprise AI.

Discover how enterprise AI is driving the Financial Markets revolution.

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AI in Financial Markets

As Financial Market firms work to digitize and transform for growth and operational efficiencies, they are aggressively innovating and differentiating, as they compete to secure a larger share of assets and create a next generation client experience. From AI trading to AI fraud detection to the benefits of a machine learning stock market— artificial intelligence helps firms to reimagine their operations. By accelerating initiatives, leveraging their own data, and delivering bottom-line results, companies using machine learning for trading cannot 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

AI Use Cases in Financial Markets

The opportunity to apply AI across financial markets is expansive. There are thousands of applications for automated machine learning to help ensure that financial market research and investment firms are empowered to make 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 competitive differentiator.
Marketing and Cross-Selling

Customized marketing campaigns increase ROI and improve response rates. 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 in their tactics. It also enables investigations to take place before losses are posted. 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 AI, 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 drive both dissatisfaction and churn.
Trading Operations

Traders are using historical transaction cost analysis (TCA) and execution data to build models that optimize offer 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

AI 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 with the greatest payoff. Using AI 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 enterprise AI-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