Euskaltel Group, a leading telecommunications company in Spain, was planning a nationwide expansion. The company needed a scalable way to use AI and machine learning to attract and retain customers, reduce the incidence of default, and identify cross-selling opportunities. Their business intelligence team had experimented with AI on a limited basis but still spent considerable time writing code. The challenge was to find a more efficient and effective way to use AI and machine learning in their workflow.

Valley Bank, a regional bank with approximately $50 billion in assets, was facing a challenge in its Anti-Money Laundering (AML) department. The bank was dealing with an overwhelming volume of false positives in its effort to uncover money laundering activities across millions of transactions. The bank's AML team was seeking to reduce the manual work involved in predictive modeling. The process of creating models manually was time-consuming, taking weeks to complete. The bank was looking for a solution that could automate its fraud detection process and manage the volume of false positives in a realistic way.

Matmut, a major player in the French insurance market, relies heavily on data to elevate nearly every area of the company. However, the company was facing challenges in deriving insights within the limits of stringent privacy regulations. Matmut’s data lab was building predictive models with a single Jupyter notebook, a process that was manual and required considerable coding. This approach was not efficient and did not foster collaboration between data scientists and the business. The company was in need of a single solution that could reduce the effort and enable collaboration.

Turo, the world’s largest car-sharing marketplace, sought to optimize its operations by leveraging data insights. The company connects guests and vehicle owners for mutual benefit across the US, Canada, and the UK. With over 1.3 million active guests and over 85,000 active hosts powering more than 160,000 active vehicles across 1,300 unique makes and models, Turo needed a way to efficiently manage its vast operations. The company aimed to optimize pricing, risk, and marketing strategies using data insights. However, the sheer scale of its operations presented a significant challenge in terms of data management and analysis.

The U.S. Army was facing a challenge of identifying funds that were potentially going to be lost due to expiring contracts. They needed an innovative AI solution that could help contracting officers accurately predict the contracts most likely to underspend their funding so they could quickly deobligate and reallocate these funds to other high priority projects. The Unliquidated Obligation (ULO) project was born out of the Army’s HQ Analytics Lab (HAL) and Deep Green OBT initiatives.

MinterEllison, a multinational top-tier law and professional services firm, was looking to grow profitably and sustainably as part of its 2025 strategy. The firm, which operates in five countries, needed a more sophisticated, predictive lens to understand what might happen, especially in the wake of the COVID-19 pandemic. The firm's existing data analytics platform was not sufficient for this task. The firm's Head of Data and Analytics, Shaheen Saud, emphasized the need for a good understanding of performance and opportunities, which prompted MinterEllison to take an innovative look at its IT and digital services infrastructure.

Embrace Home Loans, a prominent mortgage lender licensed in all 50 states and the District of Columbia, sought to optimize its marketing spend across its digital and direct mail channels. The company wanted to maximize marketing spend and increase revenue across all marketing channels. The challenge was to do so across the scale of Embrace’s operations, which was a significant task. The company needed a solution that could manage hundreds of Jupyter notebooks and run SQL queries on millions of rows of data. The solution also needed to ensure the security of Embrace’s customer data, which included risk-based and standards-based security protocols to protect all data.

Citi Ventures, the innovation arm of Citibank, is constantly on the lookout for emerging trends in technology and financial services that can help solve challenges faced by Citi and its clients. Since its inception in 2010, Citi Ventures has invested in over 100 different companies to enhance Citi’s products and services. However, the organization was seeking innovations that could solve challenges for Citi and its customers more efficiently. They were particularly interested in the field of AI and machine learning, which they saw as game-changing for the financial industry. They were looking for a solution that could empower both data scientists and business users, automating much of the modeling process and freeing up their time to focus on solving complex business problems.

Sanlam, Africa’s largest non-banking financial institution, exists with the purpose of empowering generations to be financially secure, prosperous, and confident. However, the company was facing challenges with its data science operations. The open-source AI options they were using felt cumbersome to navigate and lacked critical explainability for business stakeholders and compliance. This was hindering their ability to drive critical business value levers such as sales and client retention. The company needed a more streamlined and transparent AI solution that could help them improve their operations and deliver better results.

Freddie Mac, a company chartered by Congress in 1970 to support the U.S. housing finance system, has been facing challenges in achieving meaningful predictions and key insights to inform business decisions. The company works with hundreds of thousands of customers and mines nearly four terabytes of data. However, they found that business intelligence and manual practices didn't scale effectively across this vast customer base and data volume. As market and economic conditions change, Freddie Mac must remain flexible and continuously deliver on its commitment to affordable, adequate housing. In a sea of unstructured and semistructured data, it’s challenging to achieve meaningful predictions and key insights to inform business decisions.

Cegid, a French tech company offering cloud services and management software solutions, is facing the challenge of creating more models in less time while minimizing the technical skills and resources required. The company serves 350,000 customers across 150 countries and generates €632 in revenue. The predictive analytics team at Cegid is under pressure to meet the ever-expanding demand fueled by frequent acquisitions. The team is tasked with tackling a growing list of business challenges, including predicting the likelihood of getting paid on invoices and the propensity of customers to add services.

MAPFRE, a Spanish insurance company, operates in over 100 countries, generating €27.3 billion annually. The company's analytics team is responsible for providing advanced analytics to help make decisions on pricing, sales, retention, underwriting, and more. However, given the demand for data insights, the team found it challenging to keep pace with the many incoming requests and deliver value quickly. The team needed to expedite its time to market in tackling new business challenges.

AUTOproff, a European leader in digital dealer-to-dealer trading, was facing a challenge in scaling its operations. The company, which had more than 100,000 cars on auction in 2021, was struggling to produce car value estimates within the 20 minutes promised to customers. This task was entirely dependent on a team of skilled vehicle professionals. As the company grew, the need for scaling became increasingly important. The challenge was to automate the process of producing car value estimates to expedite the turnaround time for customers and free up the data scientists and estimators to focus on more rewarding parts of their jobs.

Decode Health, a healthcare AI company, has always relied on predictive analytics to unlock discoveries using data. However, in the early days, modeling was a slow, manual task. Analyzing a single dataset could take two to three weeks, with two to three data team members working around the clock. This exhaustive manual effort included considerable time preparing data, waiting on models, recalibrating, and waiting again. The company needed a solution that could streamline this process and deliver accurate results more quickly and cost-effectively.

Phoenix Children’s is one of the nation’s largest pediatric health systems. It provides world-class inpatient, outpatient, trauma, emergency, and urgent care to children and families for more than 38 years. The organization is continuously at the forefront of innovation and is recognized among the nation’s top-ranked children’s hospitals. Phoenix Children’s wanted to use analytics to improve both clinical and operational decisions. However, manually building a single model took the better part of a year. The healthcare system knew that a certain percentage of children who present with other health concerns may actually have undiagnosed malnutrition. If they could identify cases of malnutrition, they could intervene and influence outcomes.

Carbon Digital Bank, a financial institution serving the underserved African market, needed a way to quickly determine credit risk for individuals without prior credit. The bank also wanted to empower its data science team to take on additional business challenges. The bank had committed to a data-first strategy and looked to AI as an integral part of its decision-making. However, assessing customers' credit worthiness was a major challenge. The bank needed to expedite decisions on hundreds of thousands of loan applications every month.

Ngozi Dozie and his brother Chijioke identified a significant gap in the Nigerian financial landscape, particularly in the areas of consumer lending and credit infrastructure. Out of 100 million adults in Nigeria, over 40 million of them did not have bank accounts, and there were only about 200,000 distributed credit cards in the entire country. Commercial banks were hesitant to offer consumer loans due to the high risk associated with lending to consumers without credit. Building a credit score in a market like Nigeria is a huge challenge, with little documented financial history or asset ownership. This presented an opportunity for Carbon, the fintech company started by Ngozi and his brother, to help serve the underbanked population of Nigeria.

Trupanion, a leading provider of medical insurance for cats and dogs, was dealing with a lot of data from different aspects of their business; pricing, sales, claims projection, customer retention, and more. They did a good job of reporting metrics, but they did not yet have the technical capability to analyze that data on a deeper level for optimal decision-making. This required more sophisticated technology and a lot of time. Trupanion was looking for fast and accurate predictive modeling software that is robust enough to support all their different data and information from different functions of their business.

Catholic Education Diocese of Parramatta (CEDP) is an educational institution with 80 schools and 44,500 students across New South Wales. The institution holds a wealth of data on its students, from performance to attendance to demographics. However, CEDP lacked the internal resources to mine this data to improve student performance and advance operational goals. They sought a solution that could help them leverage this data to enhance student success and operations.

NTUC Income, a top composite insurer in Singapore, was facing rising claims costs across the insurance industry. As the cost of doing business increased, the company needed to understand the factors driving up claims costs, who was affected, and what actions to take. Furthermore, with insurance increasingly becoming a commodity, accurate price setting became more critical than ever. However, pricing analysis in insurance can be complex, repetitive, and time-consuming. The traditional method of using Generalized Linear Models (GLMs) for pricing analysis was not ideal due to several limitations. These included assumptions of a straight-line relationship between a rating factor and claim costs, time-consuming processes, and inability to analyze text in claim descriptions. The company needed a solution that could address their pricing analysis challenges and scale with their team.

DemystData, a New York-based software company, aims to 'demystify' data by providing a platform that helps clients discover, explore, and access the vast world of data. However, as datasets get larger and data sources more varied, the complexity increases, leading to more time-consuming work for the company's limited pool of data science resources. The company's clients, particularly financial institutions, are underutilizing data, leading to business decisions being made based on suboptimal or incomplete information. DemystData aims to close this gap by increasing their clients' access to new and more data.

Steward Health Care, the largest for-profit private hospital operator in the United States, was faced with the challenge of how to use predictive analytics, artificial intelligence (AI) and machine learning to derive value from the vast amount of data they are required to collect and maintain. The primary task was to improve operational efficiency across Steward’s network of 38 hospitals, with a focus on reducing costs. The company decided to address one of the most pressing challenges facing hospital operations — staffing volume. The typical hospital staffing model is set to average census and volume, leading to inefficiencies during peaks and valleys in patient volume. This results in high expenses for on-call staff and overtime pay. Steward Health Care’s CEO, Dr. Ralph de la Torre, challenged his team to find a more proactive approach.

Harmoney, a marketplace lending platform in Australasia, was facing the challenge of keeping pace with the constant innovation required to stay ahead of big banks. The company's small team of data scientists was tasked with the development and deployment of machine learning models to improve the efficiency of the personal loans market. However, the team was finding it difficult to dedicate sufficient time to predictive analytics due to their other responsibilities. Additionally, the traditional tools they were using for modeling were time-consuming and often led to distractions from the main goal of improving the business.