AI Use Cases 

Evaluate customer credit risk using application and other relevant data to improve accuracy and reduce unconscious bias in real-time underwriting decisions. Signals may vary - for example signs of hesitation whilst form filling may indicate a higher propensity for fraud.

Sales reps actions and leads will be analysed according to sales process and potential customer response to suggest the next best action

Forecast sales based on modeling of news customer conversions, existing customer revenues, etc.

Deliver emails to a target customers and automatically analyse the responses to determine "hot" leads. Let 24/7 functioning, intelligent, self improving bots handle making initial contacts to leads. High...

Advanced analytics on call data to uncover insights to increase sales effectiveness

Personalisation of sales content and analysis of its effectiveness to allow continuous improvement.

Optimise what to say at different points in the conversation and which phrases / responses are most effective to convert prospects.

Optimise sales resource assignments, product mix, commissions modelling and accounts

Use AI to enable predictive sales. Score leads and prioritise sales rep actions or other action most likely to push prospective leads further through the sales funnel (from prospect ID to completion). Sales reps actions and leads will be analysed according to sales process and potential customer response to suggest the next best action.

Monitor and analyse interactions between customers and sales to provide insights to support sales performance. Likely to be most useful in highly structured, high volume calling situations.

Create contact lists for outbound communication through automated web scraping (e.g. of social network traffic, databases or websites) or filtering based on response and sourcing analysis. Note that this is increasingly encountering regulatory challenge (eg GDPR in the EU).

Automatically sync calendar, address book, emails, phone calls and messages to the salesforce so as to optimise time focus.

Automatically follow up on unresponsive leads using targeted campaigns that will be modified depending on the lead’s behaviour or CRM data about the individual lead in question.

Identify performance and risk for pilots through flying patterns and other data. Similar analysis can be done on road drivers although the data is inevitably more likely to be complicated by ground-level interactions that would be more traceable in the air (e.g. other vehicular movement).

Identify performance and risk for drivers through driving patterns and other data. This can be used for a variety of drivers including those of taxis, HGV or delivery vehicle operators.

Using image and location tracking data to identify suspicious behaviour patterns by nautical vessels that might indicate, for example, over-fishing. This would also include multi-vessel activity to predict load transfer. Similar technological approaches might be used to identify other nautical malfeasance such as smuggling.

The rise of biometric-based security systems poses risks that fake credentials - photographs, scans, copies or even post-mortem presentation - may be offered to fool security systems. Image recognition is used to scan for and observe minute differences that may suggest the credentials are invalid.

Location and establishments hazardous to consumers and individuals might include restaurants, illegal businesses, areas prone to crime and violence and others. Hazards may be medical, environmental, human-caused or other natural phenomena.

Volumes of mail are such that it is almost impossible to consistently check for items of concern - whether smuggled drugs or packages of suspicious substances likes anthrax. Using X-rays or similar sensor technology large volumes of images can be processed to indicate which items of mail should be properly investigated.

The proliferation of security systems reliant on biometric sensors creates a need for these to be stress-tested. One way of doing this is the creation of faked credentials to fool the system. Clearly there is also a potentially criminal aspect to this work.

Whether babies in cots, horses in stables or indviduals in hospital beds CCTV can be used to monitor behaviour, stance and audio to predict potential issues or healthcare related abnormalities. Predictive messages can then be sent to trigger human intervention. CCTV reduces some of the issues associated with wearables although clearly only works in defined spaces.

The roll out of peer to peer transport apps creates potential security risks, particularly in markets where personal checks on users and drivers are less developed. Automated systems will highlight beahvioural anomalies that may indicate a developing security or health situation and trigger both back-up enquiries and potentially further action.

Typically using video or camera technology to spot individuals whose activity is at risk of causing accidents or other problems. Relevant images will be flagged for potential further intervention.

HIgh value animals can need identifying at critical moments - for example to ensure that the right horse has been entered in a race or has been provided to stud. Individual eye scans are kept on record and then a portable scanner can be used to ensure that the right aninal is present.

Search for inbuilt bias in algorithms will be a growing field. This will be driven by growing regulatory, press interest, consumer unease, potential legal challenge and a need to avoid black box AI. Management teams will also want to understand algorithm outcomes to ensure that the business is being optimised appropriately.

Even with advances in computing power there will come a moment when too much data has been gathered to be economically stored - for example during high spec science experiments. At that stage, in real time, machine learning can be used to help decide which data should be stored for analysis and which deleted.

Capture, collate and visualise multiple sources of data to build projected site layouts for architectural purposes. Data sources will include geo-relevant public data sources, private data sets and potentially sensor data.

Collate data from multiple sources (public, private and potentially real time sensor includng aerial or even satellite imagery) to build a clear visualisation of potential building site. Can be used to assess potential risks, visualise opportunities and project planning issues.

Analyse social, traffic data to measure potential impact of new building developments. Modeled outcomes might indicate hazards (e.g. fire evacuation risks) or design features for consideration (e.g. siting of facilities etc)

Accelerate product discovery based on modelling of components - potentially millions of input options can be modelled to ascertain the most promising.