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Artificial Intelligence

In 1950 the British academic Alan Turing proposed that a machine’s ability to exhibit intelligent behaviour could be tested to determine whether the activities of the machine is indistinguishable from that of a human (Muggleton 2014). The ability of a machine to exhibit intelligence has since become known as AI. Machines utilising AI are able to competently perform or mimic the cognitive functions that traditionally have been associated with humans. Modern examples of AI include computers that can beat professional players at games such as chess and Go and self-driving cars. To illustrate the significant potential of the technology some examples of the exploitation of AI in the financial services sector are described in Table 12.1.

Autor et al. (2006) opined that one possible impact as computer technologies such as AI spread across developed economies is that employment opportunities will be clustered at the top of the market based around high-wage/high-education jobs and at the bottom in low-wage jobs requiring little education. Nevertheless lower skilled jobs such as those in retailing are also likely to be impacted. Already self-service check-out lanes in supermarkets are becoming increasingly common and mobile apps are available to support all aspects of a product purchase decision. This situation implies that the need for staff in terrestrial outlets may over time be reduced.

Ford (2015) noted that in the past many low-wage jobs have been protected from automation because humans are extremely good at tasks requiring mobility, dexterity and hand-eye co-ordination. However these advantages can be expected to diminish as more affordable robots utilising AI software become available which can mimic humans in the fulfilment of various job roles. It is unlikely that ongoing advances in AI will lead to immediate job destruction and rapidly rising unemployment. Nevertheless as with the two previous Industrial Revolutions, in

Table 12.1 Examples of how AI is being utilised in the financial services sector*



Credit card security

To tackle payment card fraud a multipronged strategy has been evolved based on knowledge discovery at every stage of the card acquisition, approval and usage lifecycle

Mortgage risk analysis

Data mining techniques are used for mortgage risk analysis. This involves reviewing the client's previous financial records; this data provides an idea of the characteristics of the client, thereby helping to take a decision regarding whether or not to allow mortgage

Detection of irregularities in security price movement

Piece-wise Linear Representation and Artificial Neural Networks are used to analyse any apparently non-linear relationships between the stock closed price and various technical indexes, thereby permitting uncovering trading signals hidden in historical data

Prediction of default and bankruptcy

Bankruptcy prediction is a critically important area of decision making. Intelligent techniques are used to develop models capable of predicting business failure cases. These models employ classification methods, performance metrics, input data and datasets as the basis for interpreting the likelihood that there is an emerging threat of bankruptcy



This involves the automation of the analytical review procedures undertaken by auditors for the purpose of obtaining audit evidence through the utilisation of neural networks. The process can also be extended to cover issues such as bad debt prediction and management, risk assessment, internal control systems and assessing the quality of financial decision making

*Source: Modified from Moudud-Ul-Huq (2014)

the current third Industrial Revolution the structure and nature of job markets will change, with opportunities in some sectors being significantly diminished whilst hopefully new opportunities will arise elsewhere within the economies.

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