7 Solutions for AI in Financial Services

Dataiku Product, Scaling AI Benjamin Libman

Here at Dataiku we like to make the lives of everyone who works with data as easy as possible. Whether you devote your time to thwarting credit card fraud, detecting and stopping money laundering activities, embedding ESG frameworks across your processes, or more, you should benefit from the technologies that allow you to work smarter across the board.

That’s why we’re constantly designing new solutions, installable right on your Dataiku instance, for many of the financial services industry’s most common headaches. In this blog, we’ll highlight seven of these to help you find the one that’s right for your team.

Credit Card Fraud Detection

Companies that work on the reduction of credit card fraud are usually open for improvements but are often worried that any change could induce a disruption in their system, with potential side effects on end consumers with payments blocked or wrongly authorized. One extra challenge is the possibility to be able to quickly validate the impact of changes on real-world data. 

Our credit card fraud solution provides a unified space for fraud-fighting teams to manage business rules alongside machine learning approaches. Thanks to a pre-packaged flow, with its easy-to-use sandbox experimentation, it ensures the gains from machine learning are realized, without losing established success through existing approaches

With this solution, heads of payments/credit cards, heads of risk or fraud, fraud analysts, or data scientists are able to:

  • Use business and machine learning rules with a comprehensive fraud detection approach incorporating both in a unified scoring model. 
  • Explore data with rapid and thorough analytic insight, coupled with a powerful model insight application.
  • Use real-time API integration and alert management – finalized models can easily be deployed via API and alerts analyzed via dashboards, integrate these in case management systems, and more.

Credit Scoring

Credit scorecards are a foundational part of a credit teams’ workflow, and enhancing them with more powerful data sources and faster collaborative review is vital to retaining and expanding a customer base. Existing tools can be difficult to adapt to more sophisticated scorecard models, and future-focused approaches can often be disconnected from the current technology and needs of the team, siloing the potential benefits and preventing them from being effectively integrated into the working model that directly impacts customers. 

Dataiku’s credit scoring solution provides a unified space where existing business knowledge, machine-assisted analytics (for example, automatic search for a large number of features and feature iterations for credit signals), and real-time collaboration on credit scorecards are brought together. Credit teams can immediately benefit from the value of an machine learning-assisted approach, establishing a foundation on which to build dedicated AI credit scoring models, all while remaining connected to their current customer base and systems.

Interactive Document Intelligence for Environment, Social, and Governance (ESG)

The data sources required to effectively embed ESG into financial processes, including know your customer (KYC), trade finance, credit scoring, and investments, are many and varied. The ability to leverage unstructured data through document intelligence is critical. Currently, organizations rely on individuals to read sections of these documents, or search for relevant materials without a systematic way of categorizing and understanding the data.

Dataiku’s Interactive Document Intelligence solution for ESG automatically consolidates unstructured document data into a unified, searchable, and automatically categorized database, with insights accessible via a powerful and easy to use dashboard. Using a modular ESG keyword database (which can be enhanced or swapped out for other topics with ease) the solution can be used to tackle questions such as: 

  • What ESG topics are being addressed within a portfolio or document collection, and which are rarely tackled?
  • What firms or offerings are facing challenges or successes associated with ESG topics of interest, e.g., relating to environmental impact?
  • What documents or entities are ESG outliers according to my document collection, positive and negative?
  • What ESG trends emerge over time around topics and firms associated with them?

News Sentiment Stock Alert System

Traders, equity analysts, and portfolio managers have to leverage an ever-growing stock of information to fuel their company analysis. Of vital interest is knowing what stocks are most likely to move based on current news and public sentiment, what are the underlying news events driving volatility for a specific ticker, and what historical insights can be gained through a systematic analysis of past news events.

Automatic anomaly detection, a key component of Dataiku’s news sentiment stock alert system business solution, removes the need for costly and small-scale labeled datasets, avoids unfocused and often inefficient manual reviews, and works alongside purely automatic trading responses based on news sentiment.

An easy-to-use interface allows for immediate insights, rapid drill-down, and deeper analysis of trends, all with a few clicks. Flexible design allows for enhancement or customization to meet a team of firms specific needs. Here are some of the highlights:

  • Ready-to-use volatility scores at ticker-level based on news sentiment analysis.
  • Immediately actionable real-time insight into ticker-level market movements.
  • Comprehensive and transparent historical analysis allowing creation of more informed responses to news events.
  • Easily enhanced or enriched with business-specific data sources or focus.
  • Anomaly detection & stock price analytics including principal component analysis (PCA).

Anti-Money Laundering (AML) Alert Triage

Leveraging data across diverse processes within and across financial institutions has become a requirement in tackling the evolution of financial crime mechanisms. Several banks integrate machine learning-powered models in their AML set-ups, enabling them to reinforce their rules-based financial crime processes while preserving the best standards of explainability.

Improvements in AML processes must occur at many points in the data chain, and a modular solution that can be readily incorporated into existing flows to more efficiently process existing alerts is a means of improving detection rates and reducing alert fatigue. 

Thanks to Dataiku’s AML business solution, financial crime analysts can bolster their initial assessments through risk likelihood prioritization. Insights delivered by the solution can help AML teams assess the effectiveness of existing business rules, paving the road to further reinforcement of teams’ AML systems.

The AML Alert Triage solution provides a reusable project to modernize the handling of AML alerts. With this solution, AML case managers, model reviewers, AML team leads, and AML data scientists will be able to:

  • Prioritize investigations to tackle likely true positives first.
  • Avoid any additional regulatory burden, as no alerts are discarded.
  • Easily integrate this solution into their existing AML processes.
  • Provide insights which can be used to review effectiveness of some business rules as a distinct project.

Customer Segmentation for Banking

Leveraging a purely data-driven approach to customer segmentation introduces the possibility of new perspectives, complementing rather than replacing existing expertise. By creating a unified space where existing business knowledge and analytics (for example, on cross sell and tiering) are presented alongside new and easily generated machine learning (ML) segmentation, business teams can immediately benefit from the incremental value of an machine learning approach while preserving continuity with established methodologies and analytics.

Dataiku’s customer segmentation business solution does exactly that. Here are some of the solution’s key highlights:

  • Enrich your customer segmentation approach by blending machine learning and existing techniques, deepening product expertise, and marketing effectiveness.
  • Business-friendly explainable AI allows your team to quickly create and then immediately understand the results of machine learning-based segmentation, without complex development.
  • Powerful visual analytics clearly reveals customer segmentation trends over time, ensuring your past, current, and potential future customer mix is effectively understood by all teams.
  • Instantly actionable insights allow your marketing specialists to instantly understand revenue share, product mix, and much more, all through prebuilt cross-sell, tier, and segment analysis dashboards.

FX P&L Impact Modeling

Foreign Exchange (FX) impact modeling is one of the first steps finance teams can take towards embracing the potential of agile analytics to enhance their business partnering positioning across all dimensions: from budget building and investment decision making to precise monitoring and forecasting, at any given granularity.

Dataiku’s FX P&L business solution provides an environment to allow FX experimentation and impact modeling that is robust,  scalable, governable, and has the capacity to handle large data sets and complex business logic. The solution can play an instrumental role in allowing finance and business teams to focus on decision making rather than tedious calculation and iteration. 

Here are some of the solution highlights:

  • Simply ingest costs and revenues with full flexibility on periods, granularity and input systems.
  • Compute FX impacts across currencies and compare to various rates, e.g., budget, current, forecast.
  • Accelerate discussions between Financial Analysts and Business Owners thanks to powerful interactive dashboards with pre-built analyses.
  • Immediately begin FX rates experimentation and review results through the dedicated visual interface.
  • Empower all finance teams through a visual setup allowing any team to quickly create the project, connect datasets, and set key parameters, with scalable design.Leverage available visual analytics for immediate insights and relevant summaries sharing with business partners and management.Share a single project across multiple teams, or create copies to allow separate business lines or teams to experiment independently, and export results with ease.

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