Uses of machine learning in finance

If you wonder whether to implement AI and Machine Learning into your financial business or not, this is an article for you.

We will discuss here the advantages and disadvantages of this solution. Machine Learning is ideal for the financial service’s industry because there is always an enormous database to operate and the more data you have the better for you because due to that the AI can learn faster.

Yes, investing in Machine Learning requires some significant amount of money which often is a major concern but the payback is quick to be noticed. Here are some examples of what Machine Learning is capable of doing.

Automation of customer service

The use of Machine Learning saves your company’s energy, money and time. It can even replace regular employees by taking over the whole part of the company responsible for customer service. AI can answer e-mails and phone calls, and can also respond to requests via chat on a website. The program can teach itself to answer more and more accurately, and eventually become the master of customer service. Machine Learning can also provide the company with improved training for employees and even perform it.

But then, these solutions are obvious and do not regard only financial businesses. Well, when it comes specifically to the field of finance, AI has also a lot to offer. For example, it is very effective in detecting fraud, helping to make the best trading decisions and instantly predicting a level of credit risk.

Detecting fraud

It is sometimes hard to see small anomalies in the financial habits of customers and to be honest no one can monitor all their clients all the time. AI, on the other hand, can. It processes tons of information every second and detects those anomalies in an instance. What is more, it can not only detect but also immediately prevent fraud because of how fast and accurate it operates. Preventing fraud results in blocking suspicious operations, transactions, and accounts. Of course, if there is enough percent chance of them being abusive.

Processing information on credit risk

Automation of analyzing the solvency and credit risk of any customer benefits companies greatly. There is a really small chance of any mistake and there are no emotions involved in the process which unfortunately is essential in financial services. Generally speaking, AI protects your company from making bad financial decisions that can hurt your business. It considers many factors, some of which could seem irrelevant to a human being but all in all, make a great difference.

Analyzing the stock market

AI is capable of analyzing thousands of data all day and all night to track patterns and predict the state of the market. That is something that no human could ever achieve. Nevertheless, the results of those operations are very desirable. Every little change that has been predicted can save or earn lots of money within seconds.

Read more: https://addepto.com/finance/

One in seven Brits admit to committing fraud

A new report released today shows one in seven British adults have committed one or more types of consumer fraud, while two in three know someone who has.

There are many types of first-party fraud – including:

• Money muling – agreeing to transfer illegal funds to a third-party from their bank account, generally keeping a share for themselves
• Claimed non-delivery – ordering goods online and falsely claiming they haven’t been delivered to get a refund

The most common type of consumer fraud committed by the British public is ‘fronting’, closely followed by ‘deshopping’, which 1 in 20 (5%) admit to carrying out.

Attitudes towards first party fraud

Alarmingly, many Britons consider some types of consumer fraud as reasonable, with the highest proportion seeing ‘fronting’ as reasonable. However, the consequences of committing this type of fraud could see individuals driving without valid insurance, and in some cases, result in a criminal record.

Interestingly, ‘money muling’ is considered reasonable by one in five Britons, the consequences of which could result in individuals unable to open a bank account and obtain a mortgage, as well as a potential prison sentence.

Demographics of consumer fraud

The research revealed that younger people were more likely to take part in fraudulent activity, with 21% of 18-34 year olds admitting they have committed first-party fraud, compared to only 6% of people aged over 65.

Prevention key to reducing fraud

The report found that companies are more likely to invest their energy into detection and prosecution of consumer fraud, rather than prevention. This is despite the fact that detection can be problematic, and prevention is generally regarded to be more effective. The report argues that efforts to reduce fraud would be better directed towards awareness campaigns focused on educating consumers about different types of fraud and their consequences, such as criminal records, fines, or difficulties in obtaining banking and credit facilities.

Chief Executive Officer of Cifas, Mike Haley, who was responsible for the report, commented: ‘It’s sad to note how common fraud is among the British population, and that even more people find such acts of dishonesty acceptable.

‘Many people seem unaware that what they consider to be reasonable,  such as buying shoes to wear for a night before returning them, or adding their parent as a main driver for cheaper insurance, can be considered acts of fraud.

‘We wanted to raise awareness of the consequences what can be considered everyday fraud, such as finding it difficult to obtain a financial product or a mobile phone account, and in some cases such as being a money mule, end up with a criminal record.’

Matthew Oakley, Director of WPI Economics, who co-authored the report, said: ‘It is shocking that one in seven British adults admit to having committed first-party fraud.

‘That many people also see this as reasonable highlights the lack of understanding of fraud as a criminal and harmful act.

‘This report shines a light on some of the routes to people committing fraud and highlights how industry can work together to tackle these; in particular by making sure that fewer people see fraud as reasonable and that the opportunities to commit fraud are reduced.’