The financial industry leverages machine learning in more ways than just finding the right alpha signal. It grapples with supply chains, business processes, marketing, churn, fraud, and money laundering, all while maintaining compliance with the various regulatory frameworks it is beholden to. Due to the sheer volume of wealth being handled by the financial industry and its critical role in everyday life, it has been a lucrative target for a wide spectrum of ever-evolving bad actors. With each successive iteration of this workshop, we have attempted to capture the breadth of these actors - fraudsters, money launderers, market manipulators, and potentially nation-state-level risks. The emerging advances in Generative AI make this a particularly exciting time to host this workshop. GenAI offers groundbreaking approaches to handling the various data types prevalent in the financial sector. From a security point of view, bad actors are actively using Generative AI creatively to thwart conventional defenses (e.g. voice cloning, better synthetic identities), and this workshop's audience would benefit from commonly applicable defenses & best practices against such threats. Last but not the least, there is now an increasing willingness from the financial industry towards deeper engagement and data sharing with academia.

This workshop aims to explore the intersection of Generative AI with the rich tapestry of financial data types, seeking to uncover new methodologies and techniques that can enhance predictive analytics, fraud detection, and customer insights across the sector. By harnessing these advancements in AI, we can pave the way to not only understand customer behavior but also anticipate their needs more effectively, leading to superior customer outcomes and more personalized services. Our objective is to shed light on the challenges and opportunities presented by the diverse data formats in finance. We aim to bridge the gap between the dominance of traditional models for tabular data analysis and the emerging potential of Generative AI to revolutionize the treatment of time series, click streams, and other unstructured data forms.

The finance industry is constantly faced with an ever evolving set of challenges including credit card fraud, identity theft, network intrusion, money laundering, human trafficking, and illegal sales of firearms. There is also the newly emerging threat of fake news in financial media that can lead to distortions in trading strategies and investment decisions. In addition, traditional problems such as customer analytics, forecasting, and recommendations take on a unique flavor when applied to financial data. A number of new ideas are emerging to tackle all these problems including self-supervised learning methods, deep learning algorithms, network/graph based solutions as well as linguistic approaches. These methods must often be able to work in real-time and be able handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss both the problems faced by the financial industry and potential solutions. We plan to invite regular papers, positional papers and extended abstracts of work in progress. We will also encourage short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers.

The finance industry is constantly faced with an ever evolving set of challenges including credit card fraud, identity theft, network intrusion, money laundering, human trafficking, and illegal sales of firearms. There is also the newly emerging threat of fake news in financial media that can lead to distortions in trading strategies and investment decisions. In addition, traditional problems such as customer analytics, forecasting, and recommendations take on a unique flavor when applied to financial data. A number of new ideas are emerging to tackle all these problems including semi-supervised learning methods, deep learning algorithms, network/graph based solutions as well as linguistic approaches. These methods must often be able to work in real-time and be able handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss both the problems faced by the financial industry and potential solutions. We plan to invite regular papers, positional papers and extended abstracts of work in progress. We will also encourage short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers.

The finance industry is constantly faced with an ever evolving set of challenges including credit card fraud, identity theft, network intrusion, money laundering, human trafficking, and illegal sales of firearms. There are also newly emerging threats such as fake news in financial media that can lead to distortions in trading strategies and investment decisions. In addition, traditional problems such as customer analytics, forecasting, and recommendations take on a unique flavor when applied to financial data. A number of new ideas are emerging to tackle all these problems including semi-supervised learning methods, deep learning algorithms, network/graph based solutions as well as linguistic approaches. These methods must often be able to work in real-time and be able handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss both the problems faced by the financial industry and potential solutions. We have invited regular papers, positional papers and extended abstracts of work in progress. We have also encouraged short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. This event is the fourth in a sequence of finance related workshops we have organized at KDD since 2017.

We present a novel end-to-end trainable OCR system combining a CNN for feature extraction with 1-D LSTMs for sequence modeling. We present results on English and Arabic handwriting data, and on English machine print data, showing state-of-the-art performance. We believe that our method is simpler than existing 2D LSTM models, and will make it easier to use techniques borrowed from CNN research in computer vision to improve OCR performance.

This paper presents a novel method for sharing rectangular objects in a video conference. During these sessions, users often want to share a printed document or other planar rectangular objects (a photograph, the whiteboard on the wall, etc.) with the other participants. If the object is simply held in front of the camera (or if the camera is turned towards the object), the object will undergo perspective distortion and a rectangular object will appear in the video as a quadrilateral. In addition, a handheld document will not be steady and it will be difficult for other users to read its contents. In this paper, we present an algorithm that uses real-time predictive keystone correction to automatically lock on to object and present it with the proper orientation and shape.

This paper delves into the problem of face recognition using color as an important cue in improving the accuracy of recognition. To perform recognition of color images, we use the characteristics of a 3D color tensor to generate a color LDA subspace, which in turn can be used to recognize a new probe image. To test the accuracy of our methodology, we computed the recognition rate across two color face databases. We observe that the use of the LDA color subspace significantly improves recognition accuracy over the standard gray scale approach without sacrificing computational efficiency.

This paper proposes a new image representation scheme for database indexing. The application domain is "image-based" indexing as opposed to "content-based" indexing. In other words, given a database of images and associated data, we want to search the database for a known image so as to retrieve the data associated with the image. A concrete example is to search a database of aerial images so as to retrieve the GPS coordinates of a known scene. Our representation is based on a hierarchy of color histograms and offers a compromise between methods based on a global histogram and methods based on multiple histograms corresponding to segmented regions. Our method retains positional information better than the above schemes and naturally leads to a multi-level comparison strategy where mismatches are quickly discarded at higher levels. Our method offers a simple, easily implementable solution to the specific problem of image-based indexing.

A real-time visual recognition system, that enables a human conductor to control an electronic orchestra using gestures of a traditional conductor's baton, is described. The positions of the baton and conductor's hand are identified in a sequence of images from a pair of cameras, and tracked in 3D space. Gestures defining the musical beat are detected in the baton's trajectory, and conveyed to a sound synthesis system, as events that control the tempo and the phase of the music. Parameters that can enable the control of the volume of sound are computed from the range variations of the baton and the hand. The system's response is nearly instantaneous. The beat detection is reliable, and precise enough to be used by a professional conductor. It has been used to conduct an integrated electronic performance that combined a synthesized orchestra and animated ballet.

This paper describes a system that uses a camera and a point light source to track a user's hand in three dimensions. Using depth cues obtained from projections of the hand and its shadow, the system computes the 3D position and orientation of two fingers (thumb and pointing finger). The system recognizes one dynamic and two static gestures. Recognition and pose estimation are user independent and robust. The system operates at the rate of 60 Hz and can be used as an intuitive input interface to applications that require multi-dimensional control. Examples include 3D fly-thru's, object manipulation and computer games.

A video-based gesture recognition system can serve as a natural and accurate 3D user input device. We describe a two-camera system, that recognizes three gesture classes: two static and one dynamic. For one of these gestures (pointing), the system estimates five parameters of 3D pose: position and pointing direction. The recognition is robust, independent of the user and fast (60 Hz), and the estimated pose is very stable. We describe some of the interface applications that demonstrate the benefits of the system: control of a video game, piloting a virtual reality fly-through, and interaction with a 3D scene editor.

It% dwctibe u nouel malti-dimensionalhand ges-Pdre inier~ace system and iti use in interactive spatial erpplications.The system squires input Jtrtafrom ttto cameras that look at user's hand, ~ecogrrizes three gesti~res and tracks the hand in SD space.at the rate of 60 Rz.Five spatial parameters (position and orientation in 3D) are compatedjor indu finger and the thumb, which gives the user a simultaneous control of up to ten parameters of an application.11'edesctibe some of the applications that have 6e&ncon:tircted to dernonstiate the capabilities of this s~rstern.They include an interface to a video game, piloting a virtual fig-through over temain bg hand pointing, interacting with a 3D scene editor by 'grasping" and moving objects in space, and a portial wntrol of an articulated model of a human hand.The gesture interface makes the control of fhese application very intaitive, and simpler than using the cument input devices.

This paper presents a novel technique for the estimation of global nonrigid motion without using point correspondences. The complete description of the nonrigid motion of an object involves specifying a displacement vector at each point of the object. Such a description provides a large amount of information which needs to be processed further in order to study the global characteristics of the deformation. Nonrigid motion can be studied hierarchically in terms of a global nonrigid motion and point-by-point local nonrigid motion. The technique presented in this paper gives a method for estimating a global affine or polynomial transformation between two objects. The novelty of the technique lies in the fact that it does not use any point correspondences. Our method uses hyperquadric models to model the data and estimate the global deformation. We show that affine or polynomial transformation between two datasets can be recovered from the hyperquadric parameters. The usefulness of the technique is two-fold. First, it paves the way for viewing nonrigid motion hierarchically in terms of global and local motion. Second, it can be used as a front end to other motion-analysis techniques that assume small motion. For instance, most nonrigid motion analyse's algorithms make some assumptions on the type of nonrigid motion (conformal motion, small motion, etc) that are not always satisfied in practice. When the motion between two datasets is large, our algorithm can be used to estimate the affine transformation (which includes scale and shear) or a polynomial transformation between the two datasets which can then be used to warp the first dataset closer to the second so as to satisfy the small motion assumption. We present experiment results with real and synthetic 2D and 3D data.

Proposes a method for the estimation of point correspondences between boundaries undergoing nonrigid motion. The algorithm works in two stages. In the first stage, a global estimate of the nonrigid motion is obtained using hyperquadric models. The second stage uses this estimate to remove the global nonrigid motion (scale, shear, etc.) and then compute point correspondences between the two datasets assuming small deformations. The global part of the nonrigid motion (scale, shear, rotation and translation) is estimated by modeling the object with hyperquadrics and estimating the transformation between the hyperquadric parameters. Point correspondences are then estimated by using differential geometric properties during small deformations. Experimental results with real data are presented.

This paper discusses the applications of hyperquadric models in computer vision and focuses on their recovery from range data. Hyperquadrics are volumetric shape models that include superquadrics as a special case. A hyperquadric model can be composed of any number of terms and its geometric bound is an arbitrary convex polytope. Thus, hyperquadrics can model more complex shapes than superquadrics. Hyperquadrics also possess many other advantageous properties (compactness, semilocal control, and intuitive meaning). Our proposed algorithm starts with a rough fit using only six terms in 3D (four in 2D) and adds additional terms as necessary to improve fitting. Suitable constraints are used to ensure proper convergence. Experimental results with real 2D and 3D data are presented.>

We present a robust technique for the estimation of deformable hyperquadrics from images. Hyperquadrics are volumetric shape models that include superquadrics as a special case. Recovering hyperquadric parameters is difficult not only due to the existence of many local minima in the error function but also due to the existence of an infinite number of global minima (with zero error) that do not correspond to any meaningful shape. An algorithm that minimizes the error-of-fit function without using techniques similar to those presented here will often find itself stuck in "meaningless" minima, even with good initialization. Our algorithm exhibits good convergence behavior and is largely insensitive to initialization.

Presents a new approach for the automatic tracking of SPAMM (Spatial Modulation of Magnetization) grid in cardiac MR images and consequent estimation of deformation parameters. The tracking is utilized to extract grid points from MR images and to establish correspondences between grid points in images taken at consecutive frames. These correspondences are used with a thin plate spline model to establish a mapping from one image to the next. This mapping is then used for motion and deformation estimation. Spatio-temporal tracking of SPAMM grid is achieved by using snakes-active contour models with an associated energy functional. The authors present a minimizing strategy which is suitable for tracking the SPAMM grid. By continuously minimizing their energy functionals, the snakes lock on to and follow the in-slice motion and deformation of the SPAMM grid. The proposed algorithm was tested with excellent results on 123 images (three data sets each a multiple slice 2D, 16 phase Cine study, three data sets each a multiple slice 2D, 13 phase Cine study and three data sets each a multiple slice 2D, 12 phase Cine study).