Using Xccelerate, insurers can boost digital initiatives by upskilling employees that already possess domain expertise and analytical rigor. A.I. in insurance can benefit customers and drive operational, product and pricing efficiencies. Use cases include:
With the right knowledge, teams can model profitability and customer conversion to optimise top-line growth by analysing vast customer behavior data sets.
Train talent to streamline claim processing times and costs. Empowering employees to build models that recognise anomalies improves claim quality and reduces fraudulent claims.
Teams seeking to underwrite new products with complex demographics will benefit from machine learning knowledge to accurately predict insurability.
Manufacturers need to upgrade workforces to address their data challenges. Empowering employees with A.I. tools translates into real-time insights around asset optimisation, manufacturing conditions, visual identification and equipment calibration.
Train teams to predict failure by harnessing structured data such as electrical signals and equipment vibration. Equip your workforce with skills to develop end-to-end models that improve equipment effectiveness and cost structures.
Make quality control models a learning outcome so that teams can fix quality concerns earlier. Transform the data deluge from materials, production status and environmental variables into A.I. models that reduce lead time and cost of inspections.
Consistent improvements in demand forecasting optimises production. By empowering employees with cutting edge models, manufacturers can apply A.I. more effectively to product requirements and forecasted trends.
Leading logistic companies can upgrade their talent bases to leverage A.I. to drive higher profitability, operational effectiveness and margins.
Efficient operators can use historical data to predict freight lanes and networks. By training teams to build models, operators and teams can predict transit time with accuracy.
Teams that can build and maintain route optimization models with A.I. can increase efficiency of each delivery and improve overall bottom line.
Logistics companies that leverage technology to extract customs documents into easily readable formats can automate the entire declaration process and reduce heavy labor costs.
While the banking industry is facing increasing scrutiny and costs with regulation, incumbents are deal with new threats daily from fintechs. With access to deep tech skills, financial institutions can drive innovation in new ways:
Despite being a revolutionary technology, blockchain is in its early stages. Xccelerate can equip workforces with blockchain and A.I. skills for secure enterprise solutions.
Keeping track of investment opportunities is prone to human error. Using A.I. knowledge, teams can assist their clients more effectively.
Equipping teams with the right models allows for accurate loan default predictions, therefore servicing customers better and faster.
Fraud costs the industry $400 billion annually. Using A.I. skills, teams can automatically spot outliers and identify suspicious actions to improve the customer experience.
Upgrading talent with A.I. skills can automate a spectrum of portfolio management services and drive personalisation for the customer.
A.I. has immense potential to improve patient outcomes and save more lives, as well as improve the quality and efficiency of healthcare:
Latest advances in computer vision allows for early detection of illnesses. Leading teams can process images from X-Rays, CT and MRI scans with speed for correct diagnosis and drive higher standard of care to patients. Patient records can be used to train A.I. models and pattern recognition to rank patients by disease risk.
Deployment of A.I. can reduce the time and cost for developing effective treatments. Pharmaceutical teams can utilise the latest algorithms to analyse molecular compounds to make high probability evaluations on the effectiveness of the investigated molecules.
Using historical facility data such as patient, event, and environmental factors, Xccelerate can train teams to model likely usage of different hospital facilities. When the usage demand can be reliably predicted around ICU beds, discharge rates or new ER patients, teams can better prepare for projected usage load translating into more effective wait times, bed usage and staffing levels.
Drug-drug interaction is a major risk, particularly in complicated medical cases. By deploying A.I. to screen patient outcomes vs their medication records, patient safety is enhanced by flagging potential adverse interactions, as well as potentially identifying drug allergies which the patient is previously unaware of.
E-commerce is platform that disrupts traditional retail. Incumbents in the supply chain space can find plenty of advantages in investing in their team’s ability to deploy A.I. to drive digital transformation.
Retailer companies can upgrade talent to deploy algorithms that forecast demand during significant events and predict trends. This allows automated supply chain management to generate significant ROI by giving clients what they need on-demand.
By gathering historical data around buying habits, retailers are able to predict what customers are likely to buy. Using A.I., teams can quickly build accurate customer profiles and target efficiently.
Using computer vision, retailers can recognize in real time what customers are buying and automate cashiers. Gesture recognition even allows companies to harness models and libraries that can analyse facial behavior.
The industry is ripe for implementation of AI capabilities given the complexity of projects. There are numerous opportunities to upgrade teams to lower costs by synergizing more optimally.
Training your team with data science reinvents the BIM costing framework. Algorithms can evaluate topology, geometry and other building elements to enable classification advances.
With computer vision, construction companies can deploy technology to monitor in real time unsafe subcontractor behavior to increase the safety level of projects.
Each part of a single construction project has tight deadlines, as some parts are interconnected a delay of one would lead to the delay of the entirety of the project. With machine learning, companies can reduce the risk of delays.
The architecture workflow (project delivery, performance, billing etc…) is going through ongoing automation. Architecture firms facing competition from other players can leverage new data tools to deliver additional value to their clients.
While spreadsheets are heavily used across industries, training teams on business intelligence tools such as Power BI and Tableau will streamline the organization. Dashboards will help organize a project more efficiently and free time for management to make progress.
Despite a creative backing, A.I. can help save time with mundane tasks involved in design. Training your team on A.I. will enable them to focus on more value added actions.
Using data from past projects will help predict how long a project should take. In a competitive environment, this leverages resources to save money.
In my last blog, we went through the process of Data Analysis with Power BI. In summary…
Data Analytics seems like rocket science at first glance, especially when confused with…
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