Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Fighting financial fraud at Danske Bank with artificial intelligence

Ron Bodkin (Google), Nadeem Gulzar (Danske Bank Group)
2:35pm3:15pm Wednesday, June 28, 2017
Verticals and applications
Location: Gramercy East/West Level: Intermediate
Secondary topics:  Financial services, Machine Learning
Average rating: ****.
(4.33, 3 ratings)

Prerequisite Knowledge

  • Basic knowledge of financial fraud
  • An understanding of unsupervised machine learning

What you'll learn

  • Explore how Danske Bank uses deep learning for better fraud detection


Danske Bank, the leader in mobile payments in Denmark, is innovating with AI. Danske Bank’s existing fraud detection engine is being enhanced with deep learning algorithms that can analyze potentially tens of thousands of latent features. Danske Bank’s current system is largely based on handcrafted rules created by the business, based on intuition and some light analysis. The system is effective at blocking fraud, but it has a high rate of false positives, which is expensive and inconvenient, and it has proved impractical to update and maintain as fraudsters evolve their capabilities. Moreover, the bank understands that fraud is getting worse in the near- and long-term future due to the increased digitization of banking and the prevalence of mobile banking applications and recognizes the need to use cutting-edge techniques to engage fraudsters not where they are today but where they will be tomorrow.

Application fraud is an important emerging trend, in which machines fill in transaction forms. There is evidence that criminals are employing sophisticated machine-learning techniques to attack, so it’s critical to use sophisticated machine learning to catch fraud in banking and mobile payment transactions.

Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection. Danske Bank’s multistep program first productionizes “classic” machine learning techniques (boosted decision trees) while in parallel developing deep learning models with TensorFlow as a “challenger” to test. The system was first tested in shadow production and then in full production in a champion-challenger setup against live transactions. Ron and Nadeem explain how the bank is integrating the models with the efforts already running, giving the bank and its investigation team the ability to adapt to new patterns faster than before and taking on complex highly varying functions not present in the training examples.

Photo of Ron Bodkin

Ron Bodkin


Ron Bodkin is a technical director on the applied artificial intelligence team at Google, where he provides leadership for AI success for customers in Google’s Cloud CTO office. Ron engages deeply with Global F500 enterprises to unlock strategic value with AI, acts as executive sponsor with Google product and engineering to deliver value from AI solutions, and leads strategic initiatives working with customers and partners. Previously, Ron was the founding CEO of Think Big Analytics, a company that provides end-to-end support for enterprise big data, including data science, data engineering, advisory, and managed services and frameworks such as Kylo for enterprise data lakes. When Think Big was acquired by Teradata, Ron led global growth, the development of the Kylo open source data lake framework, and the company’s expansion to architecture consulting; he also created Teradata’s artificial intelligence incubator.

Photo of Nadeem Gulzar

Nadeem Gulzar

Danske Bank Group

Nadeem Gulzar is the head of advanced analytics and architecture at Danske Bank Group, a Nordic bank with strong roots in Denmark and a focus on becoming the most trusted financial partner in the Nordics. Nadeem has taken the lead in establishing advanced analytics and big data technologies within Danske. Previously, he worked with Credit and Marketrisk, where he headed a program to build-up capabilities to calculate risk using Monte Carlo simulation methods. Nadeem holds a BS in computer science, mathematics, and psychology and a master’s degree in computer science, both from Copenhagen University.