Erste Schritte

Leveraging Data at a Massive Scale with AI in Banking

To succeed in today’s ecosystem among fintech and other players, banks must continuously innovate by turning their data from a cost center into an asset.

Data has always been the foundation of the banking industry. What has changed in recent years, of course, is the amount of data available, and the speed at which it is processed as well as the need to quickly respond to market changes.

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Today’s Top Challenges

  • One of the biggest roadblocks to Enterprise AI in banking is not a question of putting machine learning models into production or even of creating the models themselves. Rather, it’s data management, which (while seemingly simple) is essential to enabling the organization to leverage data from the bottom up, democratizing data use across teams and roles.
  • Regulations represent an obvious and unique challenge, and while it’s true that the ways in which personal data can be obtained and used are limited, cutting-edge banks know how to enable staff to work with data within the confines of a well-defined data governance strategy by choosing the right tools.
  • Model risk management (MRM) is complex in and of itself, but even more so when risk validation teams look at models from different organizations or groups across the company, each of whom have their own individual processes, tools, etc.

Top Use Cases for AI in Banking

Fraud Detection: Whether being used to detect ATM fraud, bad check writing, or insider threat, fraud detection is all about finding patterns of interest (outliers, exceptions, peculiarities, etc.) that deviate from expected behavior within datasets. Using multiple types and sources of data is what allows banks to move beyond point anomalies into identifying more sophisticated contextual or collective anomalies that point to fraudulent activity.

„For the fraud detection project, we benchmarked the [machine learning] model against our current strategy to confirm that it had potential. We were already doing well in fraud detection before, but the new model was definitely performing even better than the current strategy.“

– Sami Bouguezzi, Data Scientist @ Marlette Funding

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Alternative Dataset Evaluation: With the increase in data available for sale, research teams need a way to quickly analyze that data to identify cleanliness and value before purchasing. Machine learning, particularly via a data science, machine learning, and AI platform, is a good use case here, as it allows research teams to quickly upload data, identify missing values, join new datasets, and run automated machine learning models to determine the predictive value of the data.

Recommending and Upselling: For financial services (and retail banking in particular), in which marketing actions are culturally product-oriented, the right message to a client is paramount. Using AI to look at large swaths of diverse data to determine what products or services should be recommended to someone at a certain moment based on the last interactions can be hugely advantageous to banks that take advantage of this form of hyper-personalization.

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Credit Risk & Loss Forecasting: Machine learning can increase predictive power by analyzing more data from more sources, faster, to make credit decisions (often better than a human analyst). In addition, by using a data science, machine learning, or AI platform, models determining credit risks and loss become more transparent and interpretable for professional staff — even those without a technical background.

And more…

  • Revenue Attribution
  • Customer Churn (& Other Marketing Use Cases)
  • Cash Management Product Risk
  • Trade Failure Prediction
  • Improvement and Automation of Processes
  • Regulatory Reporting Automation
  • Predictive Market Modeling
  • Evidence-Based Research
  • Replacing Any Rules-Based System

Starting the AI Journey

Banks don’t need to start from scratch to start the journey to Enterprise AI. The fact is that they already have many of the pieces in place, including staff across roles and business lines that are already using data to make day-to-day decisions.

For example, in trading, data science has created a new capacity for powerful analysis that (so far) few traders have taken advantage of. In such a competitive industry, one cannot thrive simply by blindly relying on information or models handed over from a quant. It’s time for traders to get into the data themselves.

AI in Banking: Get Started Now

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From actuaries to quants, top organizations aren’t letting these skills go to waste and instead are working on education and transition into roles where their deep industry knowledge can be easily leveraged and used as an advantage in the company’s overall AI strategy.

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Dataiku: The Path to Enterprise AI for Banks

Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. Dataiku moves banks along their data journey from analytics to Enterprise AI by providing:

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  • A platform for governance, including a transparent workspace from which to develop data projects within the confines of regulations.
  • A common ground for data experts (e.g., data scientists, quants, actuaries) and explorers (e.g., business analysts, risk and pricing analysts, line-of-business experts) alike.
  • Repositories of best practices for easy reproducibility and more agility.
  • Robust yet explainable AutoML features for data democratization across the organization.
  • One-click deployment to production and other automations to ease model operationalization.
  • Centralized model management.
  • A more efficient, collaborative, and scalable compliment to spreadsheets.

AutoML and Augmented Analytics as the Future of AI

Companies who successfully scale AI efforts in the next five years will undoubtedly leverage end-to-end AutoML.

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Go Further

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Dataiku: For Everyone in the Data-Powered Organization

This video breaks down the features available in Dataiku for each member of the team.

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BGL BNP Paribas: Improving Fraud Detection

See how BGL BNP Paribas was able to improve fraud detection and democratize the use of data across the organization while maintaining their high standards for security and data governance.

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Finexkap: From Raw Data to Production, 7x Faster

Finexkap’s data team packs a big punch, leveraging Dataiku to build data projects (using both integrated notebooks and visual recipes), automate processes, and push to production 7x faster.

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