How AI will change money management
For many people, the prospect of a conversation about financial matters fills their hearts with dread. Even the most honest claimant following a break-in or road traffic accident can’t help but feel judged by the questions they hear on the other end of a phone line, and then there is the seemingly endless wait to find out the status of the claim.
A new dawn for motor insurance
In what must be highly heralded news from those who would rather not have these conversations, Chinese financial services business Ping An look set to make sweeping changes to the way motor insurance claims are handled. Clients of Ping An simply snap a few pictures of the damage to their vehicle and send them using their smartphones. Ping An’s software then cross-checks the driver’s record while assessing the damage to the vehicle before offering a quote on the spot in most cases. This marks a significant departure from the way most companies cover motorists and is sure to disrupt the industry.
Changes for the better
Data is being used across all financial areas, with financial advisor software being used by many companies to help meet the day-to-day money needs of their clients, from investment advice to current account management and everything in between. The use of artificial intelligence has the dual purpose of bringing many services together for clients while also representing significant cost savings.
While artificial intelligence is a buzzword that is often used spuriously to describe many different types of software, true AI learns from the data it is given and predicts patterns based on a range of likely outcomes. This can be very useful for tracking shares and managing investments, for example. Being able to use data for a massive range of share trades quickly makes AI incredibly powerful when it comes to offering advice. Financial advisor software such as that offered at www.intelliflo.com can make a massive difference to clients’ investment performance.
Savings to be made
AI is already being used fairly successfully by apps such as Chip and Plum, which advises customers on their daily spending habits, recognizing how much they can afford to put away and when. This is achieved by learning from the spending data received by the customer and analyzing their income and fixed expenditure.