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AI in Finance: can banks get smarter?

Keyboard and Mouse are the new Bonnie and Clyde: How Can Banks Stop Today’s Cyber Criminals?

By Alexey Kutsenko is the head of digital marketing of DDI Development company. He is experienced in the development of the marketing strategy for companies in different industries. He knows how to do the right marketing and watches all current marketing and industry trends.

Alexey Kutsenko

Alexey Kutsenko

Even though ethical debates around AI are still pretty much alive, the empirical benefits of applying automation to banking processes outweigh the concerns. According to Accenture’s analysis of banks that have used AI to their workflow, an average bank can increase their profits up to 20-25%solely by adding AI. This statistics doesn’t even account for revenue for new product lines and customer retention.

Finance is undoubtedly the industry that experiences one of the gravest needs of automation. The processes become more complex, and employees can’t keep up. The regulations grow more elaborate, and it’s challenging to keep track of all new requirements. The demand for personalized interaction keeps growing, and banks’ teams don’t have the time and energy to satisfy these expectations.

Luckily, there is a solution.

The most efficient AI applications in finance

Artificial Intelligence can be applied to all fields of financial activities. AI can be used to increase the overall institution’s efficiency –  reforming account management, automating security, and supporting AI-based collaboration between team members and clients.

Also, AI increases the organization’s capacity for innovation. Automated software collects insights that serve as a base for creative solutions, simplifies product creation, and enables smart marketing campaigns.

Lowering costs is another objective of implementing AI. Outsourcing basic transactions to automated solutions, decreasing the use of human resources in consultant services and help desks, and taking care of regulatory compliance (RegTech).

Finally, Artificial Intelligence allows implementing of the expert business model. Machine learning insights collect insights for financial advising, client support (an automated solution can become a client’s manager), and retirement planning.

Primary industries and applications for Artificial Intelligence in the financial sector

Primary industries and applications for Artificial Intelligence in the financial sector

Let’s take a look at the leading areas of AI implementation and see how they are connected to the described objectives.

AI-driven smart user journeys

Using robotic process automation to increase the overall efficiency of an organization is a common practice by now. However, while it may increase the speed of the service, those efforts remain behind the curtain from the customer’s point of view. Automation can and should be used in areas that are visible to the end client.

An example of a smart user journey map

An example of a smart user journey map

  • With dynamic analytics, automated tools can collect in-depth data about their clients. This information will fuel personalized solutions and precisely targeted advertising campaigns.
  • With a voice-recognition-powered AI, banks can outsource simple voice consults to robo-advisors who sound just like humans.
  • If the bank’s support team is overloaded with work, it makes sense to send customers to the chatbot rather than making them wait. Reducing frustrating delays is a simple way to increase customer retention.

A case in point: banks and insurance companies can simplify the process of getting a mortgage. Affordability checks and short consults can be entirely performed by AI. While the human expert will still oversee the process, the simplest procedures can be quickly done by bots.

Preventing cybersecurity risks

AI and machine-learning algorithms are perfectly equipped to analyze systems and detect suspicious patterns. By training AI to analyze the transactions and website access history, bank management can identify shady behavior.

The companies which implement Artificial Intelligence in cybersecurity

The companies which implement Artificial Intelligence in cybersecurity

The main uses of AI in financial security are the following:

  • Detecting fraudulent actions that resemble bot activity or hacking threats.
  • Handling suspicious financial operations.
  • Identifying security vulnerabilities in online systems.
  • Exploring potential safety risks.

As the company scales, so grows the volume of data that needs to be analyzed. Massive amounts of work can’t be performed manually. AI, on the other hand, will handle a broad analysis with no problems.

A case in point: Danske Bank claims that after implementation of an AI-based fraud prevention software, their false positive were reduced up to 60% and the future estimates reached as much as 80%. The efficiency of fraud detention improved by 50%. All this was achieved at a reduced cost, compared to previous spendings. The workload which was before performed by human workers, now ended up fully outsourced to an automated platform.

Fraud detection for call centers

Call center fraud is on the rise. According to Pindrop, call center fraud rates jumped from one in every 2000 calls in 2015 to one in 937 in 2016. That means fraud rates increased by 113% over the previous year. Fraudsters contact a bank’s call center and exploit interactive voice response vulnerabilities and social engineering-related methods to obtain customer data. To reduce fraudulent calls, banks should focus on implementing AI-powered security solutions that detect and prevent fraud. In addition to that, it’s wiser to examine call center fraud separately.

Unfortunately, there is no such alert during the actual call center fraud

Unfortunately, there is no such alert during the actual call center fraud

How does this security threat work?

  • A fraudster pretends to be a customer and requests the essential login information. They change personal information, passwords, edit verified emails, and complete transactions.
  • The call center agent can’t tell the difference between the actual user and the deceiver. A person on the other side of the phone has just enough information to make the request seem legit.
  • The institution is forced to take responsibility for the fraud since they were technically the ones to give away the sensitive data.

AI speech analytics solves this problem quickly. The automated software analyzes the speech pattern of each client and creates a digital profile with recorded habits. This information goes to the searchable database, which gets pulled up whenever there is an incoming call.

AI speech recognition platforms include the following functionality:

  • storage of past fraudulent calls to keep track of typical patterns and help with further investigation;
  • keyword identification – by analyzing previous frauds, the software determines a red flag and watches out for them in all next calls;
  • real-time guidance with smart insights on handling the fraudulent request in real-time – it helps your team to manage such situations quickly and with ease.

Smart systems provide better advice

One of the most rapidly-evolving friends of finance is advisory. By assisting customers in managing their investments, spendings, and income, banks create a personal bond. Wall Street institutions and Silicon Valley companies have already been exploring this trend for a while and made tremendous progress.

The work of automated solution may entirely replace the employment of human assistants or enhance their work. Instead of wasting time on small tips, experts can focus on fundamental questions. This way, the banks reduced the amount of spent time while delivering the same result, or even a better one.

The examples of how AI is used for consulting in business organizations

The examples of how AI is used for consulting in business organizations

AI-based systems analyze the history of a client’s financial activity, detect positive and negative patterns, and compares it to overall economic trends (the stock dynamics, price fluctuation, investment trends). This information is turned into generic advice that, with the help of a human expert, can be personalized further. To add precision to the consult, such systems provide numeric reports and graphs.

A case in point: Morgan Stanley Wealth Management team has created a bot that updates the company’s uses on the latest market changes and provides their experts’ opinions on the topic. It’s an excellent way to promote your expertise and create a personal relationship with the client.

Final words: Embracing intelligence in finance

Artificial Intelligence is a highly-disruptive innovation that is relevant for any modern industry, but especially so in banking. With massive volumes of data processed every day, it becomes impossible for companies to take care of that manually.

Despite its high innovation potential, AI has a relatively low entry barrier. The technology is accessible, and there are a lot of talented developers who can bring this innovation to life, both in house and in outsourcing development.

Artificial Intelligence is already being implemented in multiple fintech startups

Artificial Intelligence is already being implemented in multiple fintech startups

However, implementing artificial intelligence cannot be treated as a cherry on the cake, as a slight addiction to the overall business process. To take the most out of AI’s enormous potential, you need to build solid relationships between your team and clients and use AI as an enabler of that.

Last but not least, AI is by no means a threat to human teams. In fact, the technology has a huge potential of enhancing typical manual work processing by taking the focus away from mundane tasks. Instead, you can use your team’s manager to do rewarding, meaningful jobs that are crucial for the organization. This way, your employees will feel more motivated, and the work will be done much faster.

Artificial Intelligence is a perfect addition to any innovation that you are implementing, be it blockchain, Internet of Things, or big data. When machine learning algorithms power these technologies, they work faster and more precise. So, if your company has been already on the track of implementing disruptive technologies, artificial technology is the next logical choice.

Global Banking & Finance Review


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