Strategy
Case Studies
''Rooms to go'' uses Machine Learning to offer tailored add-on options to customers
Home furnishing retailer Rooms To Go has leveraged Google Analytics to gain a better understanding of their consumers. By identifying products that are often sold together the company was able to customise and personalise their customers'' experience which lead to increased sales and improved the overall shopping experience.
Linked use case: Personalise product recommendations to target prospective customers
ANZ bank identifies high risk loans and predicts customer defaults with deep learning
ANZ bank has collaborated with Nvidia and Monash University researchers to develop deep learning technology. The neural network was trained on customer credit card data and is able to assess risk on a much more frequent basis than current practices and predict the client who are likely to default on payments. The system is currently a proof of concept as the bank has stated that it needs to fully understand how it works before it commercialises it.
Linked use case: Evaluate customer credit risk using application and other relevant data for faster and more efficient decisions
ARUP saves 790 engineering hours using machine learning to detect utility clash points planning a light rail system for Auckland
Arup, in a joint venture with Jacobs, was selected to plan a new light rail system for the city of Auckland. The assessment of utility systems clashing at different locations along the proposed rail line was automated using supervised learning algorithms, reducing the amount of engineering time which would have been required for manual checks by 790 hours.
Linked use case: Collate and evaluate site data for architectural planning
ASOS.com researchers demonstrate improved customer lifetime value predictions using neural networks and automatic feature selection but do not advise implementation due to increased cost
ASOS researchers demonstrate how the currently used customer lifetime value prediction system can be improved through the use of automatic feature selection. These predictions are used in business operations such as marketing for customising and targeting retention strategies. However, due to the increase cost associated with running the best performing system, it is not at this time considered a commercially viable solution.
Linked use case: Model and predict customer lifetime value
AXA used deep neural networks to increase the predictability of a customer large traffic accident from 40% to 78%
AXA wanted to reduce payout costs by better predicting the 1% of their customers that would have large traffic accidents resulting in payouts over $10,000. Using deep neural networks on over 70 variables, such as age and region of the drivers address, they increased the accuracy of prediction to 78% versus less than 40% with random forests.
Linked use case: Manage premium and risk pricing for underwriting
Adobe Research team investigates current approaches in machine learning for automating data cleansing and finds them inadequatee
Researchers from Adobe Research investigate the ability of metric learning techniques to automatically clean data. These approaches assume that datasets can be described in pre-defined ways, and that cleaning methods will work similarly well for similarly defined datasets. Ultimately, however, they find this not to be the case with the implication being that current cleaning standards are inadequate.
Linked use case: Automate data cleansing and validation
Alliance Boots achieves inventory savings and improved service levels with machine learning optimisation algorithm
Alliance Boots has deployed Manhattan Solutions'' replenishment and demand forecasting technology, which leverages machine learning to analyse data and produce accurate forecasts. Through the solution, it has been able to reduce inventory and stock levels while also improve customer service and productivity across Europe.
Linked use case: Optimise supply chain
An anonymous global insurance company analysed customer contact recordings and claims data to drive sales yields using natural language processing
Insurance companies have a wealth of unstructured data, such as sales calls, that could provide valuable insights if understood, categories and actioned. A global insurance company worked with Re:Infer to analyse contact recordings, claims data and employee feedback to identify key issues and sentiment using natural language processing. Focusing on sales intelligence they were able to increase sales yields by identifying behaviours that lead to successful up-sell and cross selling. They also improved product intelligence, claims and employee insights.
Linked use case: Analyse call content post-call
Australian Renewable Energy Agency improved accuracy of solar energy predictions by 31% using machine learning methods based on a distributed network
Australian Renewable Energy Agency investigates the ability of machine learning models to predict the output of photovoltaic (solar) energy at different timeframes for a network of sites. Three machine learning methods are investigated along with a baseline. Each performs differently depending on the time interval at which they provide predictions. However, basing predictions on a distributed network versus single site, as used in other methods, resulted in increased accuracy of 9-31% over existing methods.
Linked use case: Automate aircraft piloting
BHP saves $5.5M by predicting mining truck maintenance requirements with machine learning
BHP has established a Maintenance Centre of Excellence to analyse data from its machinery in order to predict equipment maintenance needs, improving the maintenance of trucks at several of its sites, saving $5.5 million in costs at one mine alone.
Linked use case: Predict problems and recommend proactive maintenance for mining, drilling and support equipment
BP reduces methane emissions by 74% and increases production volume by 20% by optimising oilfield well valve functions with AI modelling
BP is trialling a modelling system which uses current and historic oilfield data to build a simulation and test the effects opening and closing valves at different production sites on gas emissions, which has resulted in decreased methane coming from the vents by 74%, increased production volume of 20% and overall costs decrease of 22%.
Linked use case: Optimise extraction plans based on data including drilling samples and performance at historic and comparable sites
Baillie Gifford investigates the potential of AI to improve efficiencies through automating fund manager''s tasks
Billie Gifford investment management firms has begun to investigate the potential of AI algorithms to suggest wealth management strategies and provide investment advice.
Linked use case: Forecast asset pricing based on market patterns
BankMobile approves loans for young banking customers based on non-traditional data measures using Upstart''s machine learning
BankMobile is planning to implement Upstart''s online lending software which assesses loan creditworthiness by using machine learning to model risk based on alternative data. This is is intended to target younger customers who may have no traditional credit history.
Linked use case: Evaluate customer credit risk using application and other relevant data for faster and more efficient decisions
Big River Steel is predicting the availability of scrap steel using a predictive AI engine
Big River Steel has implemented Noodle.ai''s enterprise AI solution aiming to optimise operations at the company''s new metal recycling and steel production facility in Arkansas. Using machine learning models, the company is able to make predictions on the availability of its raw material, scrap steel.
Linked use case: Ensure inventory availability by predicting demand and triggering appropriate action
Big River Steel makes more accurate demand predictions using machine learning
Big River Steel has implemented Noodle.ai''s enterprise AI solution aiming to optimise operations at the company''s new metal recycling and steel production facility in Arkansas. Using machine learning models, the company is able to make demand predictions based on macroeconomic and historical data.
Linked use case: Forecast product / service demand levels
Big River Steel optimises production and minimises unplanned events in the production process process with machine learning
Big River Steel has implemented Noodle.ai''s enterprise AI solution aiming to optimise operations at the company''s new metal recycling and steel production facility in Arkansas. Using machine learning models, the company is able to predict unplanned events like breakouts and minimise their occurrence and effect on production.
Linked use case: Predict problems and recommend proactive maintenance for production equipment
Big River Steel predicts and optimises the maintenance of its machinery and equipment using machine learning.
Big River Steel has implemented Noodle.ai''s enterprise AI solution aiming to optimise operations at the company''s new metal recycling and steel production facility in Arkansas. Using machine learning models, the company is able to optimise and predict the maintenance of its machinery and equipment.
Linked use case: Predict maintenance requirements
BlackRock chooses investment opportunities to add to its portfolio based on AI recommendations
BlackRock is planning to move a significant amount of its actively managed wealth into an equity branch called Systematic Active Equities that uses AI algorithms and models to pick stocks.
Linked use case: Predict asset price movements based on greater quantities of data to inform trading strategies
BlueDot identified Wuhan pneumonia outbreak from social media posts before WHO made public announcement on COVID-19
BlueDot picked up on a cluster of “unusual pneumonia” cases happening near a market in Wuhan, China, and flagged it. This would become better known as the epicentre of what would come to be known as COVID-19. This was nine days before the World Health Organization released its statement alerting people to the emergence of a novel coronavirus in China.
Linked use case: Track and predict disease vector in general population
Bonanza Creek Energy avoids production shutdown and fines by anticipating when certain emissions are going to occur using a machine learning system
Bonanza Creek Energy has implemented a system that anticipates increasing risk of volatile organic compounds and emissions being released by monitoring real-time data from each of its production locations.
Linked use case: Predict and support mitigation of unplanned downtime
Boston Public Schools''s plan to reconfigure start times for high school students using an optimisation algorithm backfires
Boston Public Schools intended to leverage traditional AI methods of planning and analytics to reduce sleep deprivation of teenagers due to early school start times. Two MIT graduates were appointed to the task by officials, facing the trade off of minimising the number of school buses required, maximise the number of students starting school after 8am and increasing parental happiness and satisfaction. However, the proposed solution resulted in fury amongst parents as the updated schedule would affect middle and elementary students'' start times as well. Although BPS aimed to reduce inequities, with almost 85 percent of the district getting new start times, many black and brown families would be negatively affected. The proposed change by the algorithm was not implemented.
Linked use case: Optimise driver or pilot choices and path routing to reduce length, cost or environmental impact of trip
Branch grants loans to app users based on alternative data including contacts, social media, and call history and machine learning to assess credit risk
Branch is an Android mobile phone app that uses the data stored on a user''s phone to assess their creditworthiness and then deposits the approved loan automatically, allowing returning users with a built-up credit history to apply for larger loans at better rates.
Linked use case: Evaluate customer credit risk using application and other relevant data for faster and more efficient decisions
CAS reduces time spent on contract analysis for Brexit implications by 75% using a machine learning platform
CAS is using the Luminance platform to analyse contracts for clauses which could have specific Brexit implications, leading to a 75% reduction in time spent in identifying contract clauses.
Linked use case: Automate contract due diligence
Cambridge Analytica claims to have targeted voters by modelling voter propensity to persuasion through a variety of emotional state approaches
The marketing claims by Cambridge Analytica to have developed an approach to influencing public opinion by driving individualised messaging targeting key emotional states and life approaches has been somewhat walked back in the face of media and, subsequent, regulatory investigation. The role of this in key recent political campaigns including the Cruz and Trump Presidential campaigns remains a matter of dispute.
Linked use case: Predict propensity to support political causes / actors
CargoMetrics analyses satellite shipping imagery and plans to to identify investment opportunities based on the data with machine learning
CargoMetrics is an investment firm which uses satellite imagery to collect shipping data and is analyzed using proprietary algorithms in combination with other data to make commodities, equity index futures, and currencies trading decisions. It is building a machine learning system to identify investment decisions based on its various data sources.
Linked use case: Predict asset price movements based on greater quantities of data to inform trading strategies