Finance
Case Studies
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
ASOS eliminates the risk of inaccurate data entering finance systems by implementing a machine learning solution for invoice handling
ASOS has implemented a machine learning solution in order to optimise its invoice handling processes. Using Celaton''s inSTREAM™ software as a service, the company is now able to automate its their ''Purchase to Pay'' process, improve efficiency and ensure that the data entering its financial system are accurate.
Linked use case: Automate collection of banking data from non-standard documentation
Accenture redeploys after automating tasks with RPA system
Accenture has developed a system, SynOps, for analysing its various data input sources and automating things like contract review. While it has been using this system internally to automate processes in finance, marketing, accounting, and procurement, purportedly resulting in the redeployment of 40,000 staff, it is now selling it tp clients.
Linked use case: Digitise and automate processes using Robotic Process Automation (RPA)
Alibaba Group detects pigs'' pregnancy seven times quicker using facial recognition technology
Alibaba Group claims it has developed a system to identify pregnant pigs by observing their behaviour after mating. The task was previously a manual procedure were farmers had to observe each of their cow''s behaviour over a 21 day period. With the algorithm, it is now possible to determine if a pig is pregnant on the third day to increase the number of newborns and maximise production. The company expects that its system will be adopted by some Chinese farms in early 2019.
Linked use case: Tracking, monitoring and analysing livestock behaviour to optimise production
American Express identifies $2 billion in potential annual incremental fraud incidents with machine learning
American Express has over 100 million credit card customers globally representing over $1 trillion in annual charge volume. Amex used machine learning models based on an understanding of customer spending patterns alongside normal merchant purchase information. This allowed real-time detection of potential fraud resulting in an estimate $2b in potential annual incremental fraud incidents.
Linked use case: Identify fraudulent activity using unusual payment transaction patterns and other data
Baltimore Gas and Electric generated $2.8 million in economic benefit from identifying fraud and unbilled energy usage with machine learning
Baltimore Gas and Electric Company (BGE) is leveraging machine learning to identify and tackle unbilled energy usage. In doing so, the company has generated $2.8m in economic benefit and is expecting its annual economic benefit to reach $20 million. The company is using C3 IOT''s solutions, such as C3 Revenue Protection™ and C3 AMI Operations™ to improve the operation of its advanced metering infrastructure (AMI) network.
Linked use case: Identify potential fraud from utility consumers
Bank of NY Mellon reduces processing and trade entry turn around time by 88% and 66% respectively using robotic process automation.
BNY Mellon has deployed more than 20 bots since the start of 2017. Using robotic process automation, the bank uses the bots to complete repetitive tasks, from back office tasks to customer service, enabling its employees to focus on value-adding services. Using software from Blue Prism, the company has improved its processing and trade entry turn around times and estimates that it will achieve an annual saving of $300,000 through its funds transfer bots.
Linked use case: Improve administrative productivity with Robotic Process Automation
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
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
British Telecom saves an estimated £100M a year using the automated contract analysis platform RAVN
BT has been using RAVN contract analysis technology to analyse its commercial contract documents and extract key provisions from them, with estimated savings of £100m a year.
Linked use case: Support review and design of supplier contracts
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
Castle Ridge Asset Management identifies and rebalances portfolio investments among diverse asset classes using machine learning
Castle Ridge Asset Management uses proprietary machine learning to determine portfolio investments and rebalancing when necessary. It chooses among diverse asset classes, while its decision-making is ultimately supervised by human managers.
Linked use case: Automate through a robo-advisor client portfolio investments and rebalancing recommendations based on defined investment strategies
Circle Up helps startups raise funds via crowd funding by evaluating their potential and risk using machine learning
CircleUp plans to help entrepreneurs raise capital by matching them with potential investors. They analyse companies based on 90,000 data points and classifies companies based on potential and risk. They use this to predict likelihood of breakout success using machine learning. Investors can use this data to find interesting ventures.
Linked use case: Evaluate investment opportunities in early stage companies
DXC Technology has increased new business wins by 20% by automating with a contract management platform
DXC Technology is using ContractRoom''s contract lifecycle management platform to automate contract due diligence by analysing contracts to extract relevant information. This has resulted in a competitive advantage by allowing DXC to conduct due diligence and then sign new contracts for transactions quickly.
Linked use case: Automate review of contracts such as NDAs
Danone increased product demand forecast accuracy to 92% with a 55% improvement in net uplift from promotional events using machine learning
Danone has improved the reliability of forecasting increased demand for products as a result of its promotional efforts (uplift) through implementing the ToolsGroup system which analyses demand variability. The system draws in information from point-of-sale feeds in hypermarkets
Linked use case: Forecast product / service demand levels
Danone reduces forecast error and lost sales by 20 and 30 percent respectively and achieves a 10 point ROI improvement in promotions with machine learning
Danone implemented ToolsGroup''s machine learning solution with the aim of identifying how promotional and media events affect their sales. They also needed to secure a more accurate forecast of demand, which is necessary given that their fresh products face volatile demand and a short shelf life. The solution leverages machine learning to predict demand variability and planning. Danone managed to create an efficient planning coordination between various departments such as marketing, sales, account management, supply chain and finance and has also reported a 20% reduction in forecast error, 30% reduction in lost sales, and a 10 point ROI improvement in promotions.
Linked use case: Forecast product / service demand levels
Danske Bank plans to determine liquidity risk using Bloomberg''s machine learning product LQA
Danske Bank announced it would use Bloomberg''s machine learning LQA product to assist in determining its liquidity risk as part of regulatory compliance efforts.
Linked use case: Track risk exposures to ensure capital requirements monitored
Dell reduces costs while ensuring contractual obligations are met with contract analysis automation
Dell uses Axiom to extract clause details and other metadata from legacy systems of 35,000 sell-side contracts. This automation allows for quicker employee research into contractual obligations to inform senior management decision-making.
Linked use case: Automate contract due diligence
Deutsche Bank automates 30 to 70 percent of back- and mid-office processes using robotics process automation
Deutsche Bank''s Innovation Lab has developed robotic process automation (RPA) to process manual transactions. Deutsche Bank is using RPA to automate functions such as trade finance, cash operations, loan operations, and tax.
Linked use case: Digitise and automate processes using Robotic Process Automation (RPA)
Deutsche Bank implements AI trading platform for predicting equity prices and volume
Deutsche Bank is implementing an AI platform which aims to better predict equity pricing and volume trading, in an effort to indicate best execution for clients which is now required under MiFID II.
Linked use case: Forecast asset pricing based on market patterns
EY develops embedded audit analytics system, Helix, to automate auditing complex transactions
EY Helix is a machine learning powered embedded audit analytics system developed to automate analysis of transactions such as general ledger transactions and trade payables / receivables to detect mismatches and errors.
Linked use case: Accelerate claims processing by automating processes
FICO reduces bank''s losses on delinquent customers by up to 25% with machine learning that predicts consumer credit risk
FICO reduces bank''s losses on delinquent customers by up to 25% with machine learning that predicts consumer credit risk. They used random forest and support vector machine learning algorithms that analysed debt-to-income levels combined with banking transactions.
Linked use case: Analyse credit risk of individual customers
FSB saves loan application time for b2b lending using algorithms to match borrowers with lenders
The FSB fundraising platform was developed to match borrowers with potential lenders based on information inputted by the borrowers regarding their business and loan expectations. Lenders are sent anonymous borrow information and then register their interest, with the available options then borrowers are provided a list of potential lenders to choose from.
Linked use case: Automate credit risk profiling to support fundraising by small businesses through crowdfunding platform
Fox predicts likelihood of watching movies at theatres based on trailers with machine learning
20th Century Fox has collaborated with Google Cloud to predict how likely people are to see movies in theaters based on their trailers. Merlin, the machine learning system, is capable of recognising patterns in movies to understand their scenes. After describing a scene with objects like "car" it can use other movies and attendance records to make predictions on what people who have watched this movie are likely to watch next.
Linked use case: Forecast product / service demand levels