Published on 4th April 2019
The finance industry has often had difficulty adapting to cutting edge technology. The need for unbreachable security structures combined with strict compliance to stringent government regulation has given the sector an unfair reputation for resisting technical change.
However, in the age of internet banking and mobile services, it's clear that fintech is not only essential, it's going to continue disrupting the finance market in ways we hadn't thought possible.
What are they?
Chatbots are computer programs that use a variety of technologies to simulate human conversation. Conversation can be relayed either in message form or as a voice interface, for example, Siri or Amazon Al
Sophisticated AI-powered chatbots, or virtual assistants, are now equipped with natural language processing abilities which enable them to replicate a human interaction with customers. True to their name they can quite literally chat to users and answer relevant questions.
How will they be used in the finance industry?
Chatbots are starting to become a feature in the finance industry due to their ability to provide a frictionless medium for instant communication with customers. Specifically, questions and queries can be answered immediately in a convenient chat outlet on mobiles or computer screens. Furthermore, chatbots with machine learning capabilities have the potential to include advanced security features such as voice recognition. They can also take into account recorded knowledge of the customer's individual needs to provide them with personalised financial information, such as account status or potential investing options.
There are already some great examples of chatbots being used in Australian financial institutions. For example, the Commonwealth Bank launched its first AI enabled chatbot in January 2018. Named Ceba, the virtual assistant brings functional utility to consumers through its ability to not only supply information for banking customers but also provide more than 200 banking tasks in real-time. A big plus is that it's also available 24 hours a day to deal with questions and tasks such as helping customers to pay bills, activate debit cards or open new accounts.
It is also worth noting that Ceba, like other useful chatbots, is able to link seamlessly to human operators when asked to deal with more complex tasks such as reporting fraud or applying for a product.
2. Artificial intelligence
There are many varied ways in which machine learning is playing an integral role in the current workings of the finance sector. Systems such as automated trading, portfolio management and underwriting are already starting to incorporate AI systems. The growing popularity of robo investment advisors, and the astonishing accuracy of machine learning customer intelligence systems suggests that AI will continue to grow and assist a wide number of data based finance tasks.
What is AI?
AI has been around since 1955, but the recent advances in computing power, data proliferation and algorithmic development have made what was once theory actively possible today. AI can be broadly defined as a machine or, more accurately, an algorithm that has the ability to perform complex tasks typically associated with human minds, such as learning, problem-solving or interacting with the environment.
There are any computing fields which fall within the umbrella term of AI. The best-known examples are:
How will AI be used in the finance industry?
There are many varied ways in which machine learning is playing an integral role in the current workings of the finance sector. Systems such as automated trading, portfolio management and underwriting are already starting to incorporate AI systems. The growing popularity of robo investment advisors and the astonishing accuracy of machine learning customer intelligence systems suggests that AI will continue to grow and assist a wide number of data based finance tasks.
3. Cyber security
With the growth of digital financial transactions and management, the potential for online security risks and fraud has become a central concern for the financial industry. PwC's 2018 Global Economic Crime and Fraud Survey reports that forty-nine percent of global organisations say they've experienced economic crime in the past two years. Better, faster and more accurate security systems are now needed more than ever.
One of the main hurdles large financial organisations are coming up against in the digital world is the sheer amount of information and data they must parse in order to accurately detect fraud. As a result, static rule based systems are not flexible enough or fast adapting enough for the variety of digital transactions that people now undertake. For example, establishing a definition of 'normal transaction activity' for a user in a rule-based system can result in people being blocked from their accounts for actions which only slightly deviate from their day to day spending habits, such as booking a holiday.
Fortunately using machine learning capabilities, it is now possible to analyse large volumes of business data and monitor the effectiveness of the internal control systems. A machine-learning-based approach allows banks to augment their algorithmic rules-based approach towards surveillance and risk management. By constantly learning on the job, machine learning systems can keep fraud detection rules updated to be more vigilant.
If you want to learn more about transformations within the finance industry get in touchwith the recruitment specialists at Ambition today. We have experience working alongside a range of finance specialities, including Strategy & Planning Risk, Audit & Compliance, Corporate Accounting, Commercial Analysis and Tax & Treasury. Whatever your interests our specialists can help you find what you're looking for.