A significant challenge in credit risk models for underwriting is the presence of bias in model training data. When most credit risk models are built using only applicants who had been funded for credit, such non-random sampling predominantly influenced by credit policymakers and previous loan performances may introduce sampling bias to the models, and thus alter their prediction of default on loan repayment when screening applications from prospective borrowers. In this paper, we propose a novel data augmentation method that aims to identify and pseudo-label parts of the historically declined loan applications to mitigate sampling bias in the training data. We also introduce a new measure to assess the performance from the business perspective, loan application approval rates at various loan default rate levels. Our proposed methods were compared to the original supervised learning model and the traditional sampling issue remedy techniques in the industry. The experiment and early production results from deployed model show that self-training method with calibrated probability as data augmentation selection criteria improved the ability of credit scoring to differentiate default loan applications and, more importantly, can increase loan approval rate up to 8.8\%, while keeping similar default rate comparing to baselines. The results demonstrate practical implications on how future underwriting model development processes should follow.

Machine learning applied to financial transaction records can predict how likely a small business is to repay a loan. For this purpose we compared a traditional scorecard credit risk model against various machine learning models and found that XGBoost with monotonic constraints outperformed scorecard model by 7% in K-S statistic. To deploy such a machine learning model in production for loan application risk scoring it must comply with lending industry regulations that require lenders to provide understandable and specific reasons for credit decisions. Thus we also developed a loan decision explanation technique based on the ideas of WoE and SHAP. Our research was carried out using a historical dataset of tens of thousands of loans and millions of associated financial transactions. The credit risk scoring model based on XGBoost with monotonic constraints and SHAP explanations described in this paper have been deployed by QuickBooks Capital to assess incoming loan applications since July 2019.

A major part of financial accounting involves tracking and organizing business transactions over and over each month and hence automation of this task is of significant value to the users of accounting software. In this paper we present a large-scale recommendation system that successfully recommends company specific categories for several million small businesses in US, UK, Australia, Canada, India and France and handles billions of financial transactions each year. Our system uses machine learning to combine fragments of information from millions of users in a manner that allows us to accurately recommend user-specific Chart of Accounts categories. Accounts are handled even if named using abbreviations or in a foreign language. Transactions are handled even if a given user has never categorized a transaction like that before. The development of such a system and testing it at scale over billions of transactions is a first in the financial industry.

Spin-Torque Transfer RAM (STT-RAM) is a promising candidate for SRAM replacement because of its excellent features, such as fast read access, high density, low leakage power, and CMOS technology compatibility. However, wide adoption of STT-RAM as cache memories is impeded by its long write latency and high write power. Recent work proposed improving the write performance through relaxing the retention time of STT-RAM cells. The resultant volatile STT-RAM needs to be periodically refreshed to prevent data loss. When volatile STT-RAM is applied as the last-level cache (LLC) in chip multiprocessor (CMP) systems, frequent refresh operations could dissipate significant extra energy. In addition, refresh operations could severely conflict with normal read/write operations to degrade overall system performance. Therefore, minimizing the performance impact caused by refresh operations is crucial for the adoption of volatile STT-RAM. In this article, we propose Cache-Coherence-Enabled Adaptive Refresh (CCear) to minimize the number of refresh operations for volatile STT-RAM, adopted as the LLC for CMP systems. Specifically, CCear interacts with cache coherence protocol and cache management policy to minimize the number of refresh operations on volatile STT-RAM caches. Full-system simulation results show that CCear performs close to an ideal refresh policy with low overhead. Compared with state-of-the-art refresh policies, CCear simultaneously improves the system performance and reduces the energy consumption. Moreover, the performance of CCear could be further enhanced using small filter caches to accommodate the not-refreshed private STT-RAM blocks.