Financial Services
Case Studies
Newsfor Financial Services
A large european bank identifies issues in post-trade operations by analysing mailboxes with the use of machine learning
A large european bank is leveraging re:infer''s technology to identify inefficiencies from communication data. By using supervised and unsupervised learning the system analysed 300 of the bank''s shared mailboxes to identify issues in operations. The system then conducted a more targeted analysis of the fixed income mailboxes to identify and quantify specific issues that needed attention. The bank identified several areas for improvement and implemented the necessary targeted solutions to improve efficiency.
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
AXA UK saves 18,000 people hours in six months by deploying bots to handle repetitive tasks
AXA UK has successfully deployed bots to handle repetitive tasks. Over the last six months the insurance company has leveraged 13 software bots that assist its employees in three departments, the customer property claims, the commercial property and the liability department. The three bots that were named by employees, Harry, Bert and Lenny, helped the staff in tasks like matching customer correspondence with the relevant claims record. The specific task required the bot 42 seconds, while a human needs about four minutes, thus saving the company 18,000 people hours, which translates to about £140,000 in efficiency and productivity gains.
Linked use case: Digitise and automate processes using Robotic Process Automation (RPA)
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
Admiral automobile insurance planned to offer discounts to drivers based on data in their Facebook profiles but were prevented by Facebook
Admiral insurance had planned to analyse drivers'' Facebook data to offer discounts to those customers deemed less risky; Facebook intervened and prevented the project from going live as it was a violation of its terms of services.
Linked use case: Enhance motor insurance by predicting driving behaviour through the use of language in Facebook posts
Aegon monitors spokespeople, competitors and industry topics using machine learning
Aegon has leveraged Signal AI''s machine learning technology to monitor the insurance industry''s news and measure press coverage effectiveness. By analysing headlines and identifying common themes, Aegon creates relevant and engaging content and optimise their PR campaigns.
Linked use case: Scan social media to discover references to product and competitors for product management purposes
Aidyia fully automates hedge fund trading using genetic evolutionary and other algorithms claiming a 2% return
Aidiya launched a fully automated hedge fund in 2015 that trades in the US stock market based on evolutionary and other algorithms. Their aim is to predict stock price movements over the longer term using data from various sources. In two years the fund has achieved 2% returns which is less than many human managed funds.
Linked use case: Automate through a robo-advisor client portfolio investments and rebalancing recommendations based on defined investment strategies
Allstate is offering pay-as-you-drive plans based on measurement and analysis of customer real-time driving behaviour resulting in up to 30% reduction in premiums
Allstate insurance offers pay-as-you-drive plans in 30 US stages that reflect miles driven along with customer driving behaviour such as braking style, time of day and speed. Using sensors in the car along with advanced analytics drivers can lower premiums by up to 30%. Allstate better aligns premiums and risk with customers.
Linked use case: Create more personalised insurance pricing based on actual monitored customer behaviour
American Express Australia used machine learning to identify 24% of customer accounts that would close within four months allowing them to take preventative save actions
American Express has over 100 million credit card customers globally representing over $1 trillion in annual charge volume. The Australian company used advanced data analytics and machine learning to analyse historical transactions along with 115 variables to forecast customer churn. They were able to identify 24% of accounts that would close within four months allowing them to take preventative save actions.
Linked use case: Predict and drive customer retention and churn management
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
American Express increased new online customer acquisition by 40% through machine learning targeted marketing savings significant direct mail costs
American Express has over 100 million credit card customers globally representing over $1 trillion in annual charge volume. Amex used machine learning modelling to better target and acquire customers online. This resulted in 40% increase in online acquisition and a decrease in direct mail campaign costs.
Linked use case: Optimise customer acquisition targeting
An Anonymous UK Insurance firm demonstrates that it can better assess the risk of insuring companies by automatically determining relevant UK court cases with deep neural networks
A UK insurance firm wanted to improve its risk assessment of insured companies by automatically reviewing court case listings that might involve their clients. This would help better align risk premiums at the time of renewal or claims. Using EvolutionAI natural language processing they were able to scan court documents and identify relevant companies and cases even if the company name spellings and use were inconsistent and varied.
Linked use case: Lower the loss ratio for insurance companies through portfolio management
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
Ant Financial automates vehicle damage assessment to assist automobile insurers using image recognition
Ant Financial introduced an AI image recognition system for insurers which assesses external automobile damage and sets claims amounts based on the assessment. The system uses image data which automates part of the claims assessment process.
Linked use case: Recognise and categorise type of damage to predict insurance claim valuation through accessing photographic data
Aviva accelerates from 400 to 10 days post-merger organisational planning and employee data integration with the use of machine learning
Aviva wanted to accelerate the post merger integration with Friend''s Life. They used Concentra''s AI powered OrgVue to help design a new integrated organisation and merge employee data from disparate systems. Time was reduced from a typically 400 days to ten.
Linked use case: Optimise staff and resource planning
Aviva offers up to 30% discounts on insurance premiums based on actual driving behaviour that is monitored and analysed through machine learning
Aviva offers up to 30% discounts on insurance premiums based on machine learning analysis of actual driving behaviour. With an app Aviva is able to measure individual customer driving acceleration, cornering and braking. Driving behaviour is correlated to risk and premiums through advanced analytics. Those with lower risk driving are offered lower premiums.
Linked use case: Enhance motor insurance by predicting driving behaviour through the use of language in Facebook posts
Baidu does underwriting for consumers with limited credit history using machine learning
Baidu partners Zest Finance to use their machine learning platform to underwrite credit risk for consumers with little credit history. The platform analyses data such as payment and purchase history, customer support data etc. They also analyse variables such as how customers fill out forms, how they navigate websites, whether they are being honest about reporting income etc.
Linked use case: Evaluate customer credit risk using application and other relevant data for faster and more efficient decisions
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
Bank of America enhances its published currency research with machine learning
Bank of America is the first among three biggest U.S. banks to provide machine learning based currency research for the foreign exchange (FX) market. In fear of market turmoil, following political issues in Italy in the beginning of summer 2017, the bank began using machine learning programs to advise clients on what to sell and buy. To predict how the euro-dollar currency pair could possibly perform, BoA trained its system using both supervised and unsupervised learning on data, such as government spending and consumer confidence. Its inference was that in the aftermath of the Italian election the euro would likely weaken.
Linked use case: Compose and conduct market research
Bank of America improved call centre completion time by 23% by tracking and analysing behavioural data with machine learning that identified employee performance increases when they network and share ideas
Bank of America used smart badges to track call centre employee activity with smart badges. They recorded details such as employee office movement, who they interacted with, their tone of voice and whether they leaned into listen or leaned back. The data was analysed with machine learning and they identified that "network cohesiveness" could drive performance as employees were more likely to share ideas and help each other. This resulted in improved call centre completion time by 23%.
Linked use case: Map physical behaviour and interaction across the organisation to Improve team productivity
Bank of England analyses limit order book data using machine learning
Bank of England ran a Proof of Concept (POC) with BMLL Technologies to explore the capabilities of machine learning in analysing limit order book data from a number of trading exchanges. The Bank evaluated an alpha version of the company''s machine learning API (application programming interface) programmed to recognise patterns in the data. The Bank has noted improvements for the company following their experience of the product and identified that such technology could be beneficial for their operations in the future.
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
Bank of the West announced implementation of Pindrop''s machine learning fraud detection software in its call centres
Bank of the West has turned to Pindrop to improve security and fraud detection for its call centre customer service by analysing customer voices and other call data during the phone call to determine caller authenticity.
Linked use case: Detect potentially fraudulent or nefarious users
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
Barclays automates processing and reduces bad debt provisioning by millions using Robotic Provision Automation
Barclays implemented RPA across a wide range of processes including accounts receivables, fraudulent account closure, loan application opening, ''right of setoff'', etc automating documentation process.
Linked use case: Digitise and automate processes using Robotic Process Automation (RPA)