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4 applications of artificial intelligence in banking and financial services

Let’s face it, when you hear the words “technological innovation” the financial services industry is probably not the first sector that leaps to mind. That coveted spot is usually reserved for names like Grab or Google. But in reality, the finance industry is becoming the vanguard of technology-driven innovation, particularly in Asia. After all, traditional banks and insurance companies not only have the resources and capital, but they also have access to rich and comprehensive amounts of data. This is most evident in the adoption of artificial intelligence (AI) across the financial services sector.

In fact, the use of AI has been steadily permeating through all facets of the financial service ecosystem, from customer-facing front offices all the way to operational back offices. The frontrunners, which are leading the AI charge, are learning from and adopting the creed of the digital natives like the tech giants. The rest will struggle to remain competitive and relevant.

Here are four examples of applications of artificial intelligence in finance industries that demonstrate why and how AI adoption in financial services will soon become a ‘must have’ instead of a ‘nice to have’.

Application of Artificial Intelligence #1: Increasing efficiency and preventing fraud in claims processing

If insurance companies are a ship, then fraudulent claims are a hole in the hull—a giant leak they must constantly try to plug. The FBI estimates that in the US alone, fraudulent claims cost the industry US$40 billion a year (and that’s excluding health insurance). In the tiny island of Singapore, the General Insurance Association states that one in five claims are either false or inflated, costing them approximately US$101 million annually. In the UK, ABI reports that the number balloons to US$1.7 billion.

Clearly, improvements in fraud detection will result in a tangible impact to the bottom line. But here, insurers run into the speed/accuracy dilemma. The current human-centric processes mean that more stringent fraud prevention procedures take more time. And their customers—the majority of which are making legitimate claims—expect speedy resolutions. Until recently, the only way to resolve this dilemma was by simply throwing more financial resources at it and increasing the number of claims processing staff. But that is a very expensive answer. 

Fortunately, AI is emerging as a method to resolve the dilemma in a cost-effective fashion. Similar to credit assessments, AI algorithms now enable insurers to cut down claims processing times while plugging leakages from fraudulent claims. This is accomplished by tapping into both structured and unstructured data (e.g. social media) in tandem with machine learning and natural language processing algorithms.

Two such solutions available at AiDA Technologies include  AiDA Smart Claims and AiDA Smart Agency. AiDA Smart Claims drastically cuts standard claims processing times AiDA Smart Agency can identify  fraud on the agents’ side, including the most common kind of fraud, premium misappropriation.

Application of Artificial Intelligence #2: Augmented credit assessment and underwriting

Robust credit assessment and underwriting processes are the foundation of banking and insurance industries. Improper processes can lead to risk management failures. The subprime mortgage meltdown that precipitated the global financial crisis is one notable example. This highlights the importance of judicious credit and underwriting decisions.

The conventional structure of risk assessment is human-centric decision making aided by various risk models—often based on infrequently updated data sources. This can result in an incomplete view of the actual risks involved which can cause two equally detrimental effects: approving bad credit risks (dragging down the balance sheet) or rejecting good ones (loss of market share). The customers on the other hand are forced to put up with long processing times and potential mispricing.

AiDA’s Smart Risk and AiDA’s Smart Lending technology is helping to solve both these issues. AiDA Smart Risk is capable of drawing on both traditional and alternative data sources—such as social media, current news, credit card and business transaction information—to deliver a more holistic risk profile. That means banks and insurance companies win from stronger and more efficient risk-modelling, leading to improved market share and healthier balance sheets, while customers win from quicker processing times and more accurate risk-based pricing.

AiDA’s Smart Lending enables banks to streamline their credit assessment process and slash loan processing times from weeks to days. At the same time, it augments processes by incorporating alternative data sources that cannot be accounted for in traditional credit-risk modelling.

Application of Artificial Intelligence #3: Strengthening compliance procedures

Post-financial crisis, regulators understandably expect more from financial institutions. While necessary, Accenture states that this entails ever-increasing compliance costs—a trend not expected to abate anytime soon. And the costs of compliance lapses are very real; in 2019 alone the total fines for breaching anti money-laundering regulations stood at a whopping US$8 billion. Or consider the LIBOR scandal of 2012. Total fines from such market manipulation maneuvers cost the banks about US$9 billion.

The conventional methods of detecting such suspicious transactions or market manipulation attempts are much like traditional credit assessment processes—heavily human-centric and assisted by legacy rules-based systems. This results in escalating costs, excessive false positives, potentially missed red flags, and a greater chance for the customer to become a victim of transaction or identity fraud. It’s a high-stakes lose-lose scenario.

AI solutions like AiDA Smart Risk provide financial institutions the capability to not only analyse huge volumes of transaction data, but also to detect anomalous patterns unrestricted by predetermined rules. This can then be escalated to human-level decision makers for further review. More importantly, the result of that review can be fed back to the algorithm, which will then allow for more accurate fraud detection—a form of machine learning called reinforcement learning. Over time, compliance procedures will only become more robust, but without a commensurate increase in costs.

Stronger compliance procedures equate to sounder risk management—a critical objective for financial institutions. The ideal implementation of AI in this area is when it can be integrated into an end-to-end risk management process. AiDA’s Smart Risk is a solution designed to do exactly this. It provides banks and insurance companies with an evolving algorithm that analyses both traditional and alternative data sources to detect compliance offenses in an accurate, efficient, and cost-effective manner. Given the continually growing importance of the financial ecosystem to the global economy, sound risk management is imperative not just for individual companies but for the ecosystem and economy as a whole.

Another important part of overall risk management is financial sales risk—the risk of selling financial products inappropriate to customers’ needs. The global financial crisis was exacerbated by practices such as selling risky mortgage-backed securities and their related derivative products to institutional investors like pension funds who were looking for safer investments. This can happen on a large scale or a smaller one. Solutions like AiDA’s Smart Agency help financial services companies avoid the risk of mis-selling, which can, at the very least, jeopardise their reputations.

AiDA Technologies’ work in this area was recently recognised when we earned a spot on MEDICI’s Top-21 RegTech Award. This award identifies technology leaders that are building the future of regulatory compliance by helping businesses comply with regulations more efficiently and less expensively.

Application of Artificial Intelligence #4: Personalised frontend and customer relationship services

Building strong customer relationships is at the heart of the financial industry. Customers want to do business with banks and insurers who understand that their needs are individual and unique—they want personalised services. The problem is, personalisation is expensive, which is why it is often reserved for higher-tier customers. Insurers on the other hand, need to rely on a network of agents to gauge each customers’ needs and market to them accordingly.

AI is now enabling banks and insurance companies to deliver such personalised services without the attendant costs. Powered by AI, tools such as machine learning, deep learning, and natural language processing, are helping today’s chatbots and virtual assistants to communicate contextually with customers, provide personalised advice and product recommendations.

The customer benefits from quick, real-time service, while the institution saves on costs—not just in customer service but also sales and marketing expenditure. Thanks to AI, financial institutions can now reduce the ‘service gap’ between their top-tier and mass market customers in an effective and cost-efficient manner.

AiDA’s Smart Engagement is an example of this. Using predictive models based on extensive data analysis, AiDA Smart Engagement can identify highly probable cross-selling and up-selling opportunities. By anticipating customers’ needs, companies can be proactive, instead of reactive. As Steve Jobs once said, “Our job is to figure out what they’re going to want before they do”.

The Applications of artificial intelligence are only the tip of the iceberg

While the above applications of artificial intelligence are undoubtedly critical, they are only the tip of the iceberg. AI is also being actively used in other areas of the financial industry such as capital markets automation, portfolio management, and algorithmic trading. Thus, it is clear that the AI race in the financial industry will only intensify. Companies who remain complacent in deploying AI solutions risk being left behind, while the forward-looking frontrunners will race even further ahead. For banks and insurance companies who are trying to unlock the potential of their data, AiDA Technologies specialises in making intelligent sense of data. Contact us to request a free proof of concept.