In this last article of the 'Big Data in Finance' series, we look into how Big Data is impacting the customer experience. At the face of it, data is generated from user transactions, and the analytics algorithms that decipher patterns in the data could help improve decision-making. Such insights help to organize banking models around the needs of users to enhance the user experience. However, all the benefits that could accrue from Big Data, coupled with machine learning and AI, come at a tremendous cost both financially and in terms of complexity. Most financial institutions are looking to extract value from their massive data but few of them have the necessary expertise.
Mastering the algorithms of Big Data insights is not sufficient to guarantee the best user experience. There ought to be solid business KPIs to avoid budget drains. It all starts with a comprehensive understanding of the user and machine learning tools. Such is the basis for a successful transformation and can help cut down costs, complexity, increase value, and come up with compromises. Let's take a look at some key areas where Big Data is helping to transform.
The concept of contactless payments is increasingly becoming significant to customers today. Powered by Near Field Communication (NFC) technology, a new breed of payment channels including Alipay and others, has greatly improved the process of moving money. The role of Big Data in this transformation is almost a central one. By analyzing user trends, usage patterns, and the types of devices used, it allowed payment service providers to incorporate these unused channels much to the benefit of users. In the end, users enjoy a fast, efficient, and convenient payment solution embedded on smart wearables or smartphones.
Solutions of this nature are prominent in the market place and are tilting the angle of innovation in ways not thought possible. It only proves how important data is impacting the sector.
Before Big Data, identifying loyal customers was a tedious process prone to errors and bias. Presently, financial institutions have massive data about their customers and the tools to improve the reward system. By analyzing behavior, interactions, and spending patterns, companies can stay on top of their game by tailoring rewards, offering discounts and freebies. Owing to the efficient manner that the whole process is conducted and the personalized rewards, the outcome is a better customer experience that creates customer loyalty. Alibaba Group is experienced in personalized reward systems from Taobao, AliExpress, Alibaba Cloud, to Alibaba.com. It is also common among large technology companies such as Apple.
Machine learning algorithms can be trained to help with underwriting and credit scoring problems. Big Data containing thousands of customer profiles is used to train such models, which in turn perform complex analysis of the situation at hand to underwrite real scenarios as well as issue credit scores. Human intervention on such systems is rather limited in speeding up the service. Financial institutions have plenty of information about users from transaction activities, incomes, behavior, and can also leverage utility information to create machine learning models.
Kenya is the home of mobile innovation and presents an interesting case study. Recently, the introduction of credit reference bureaus in Kenya has sparked the growth of mobile-only lenders and insurance providers. Such services have become popular for their convenience. With access to borrowing histories of users from banks and other financial institutions, such lenders can accurately predict risks and therefore, lend to thousands of people without physical vetting. Safaricom, the largest telecommunications company in Kenya, has also been able to launch several mobile money tools such as MShwari, thanks to machine learning and credit ratings.
Automation of processes has been the most impactful outcome of machine learning. With massive amounts of data available to train models on various outcomes and possibilities in manual, repetitive tasks, tasks have been automated to improve efficiency and productivity. Some of the prominent applications include:
Intelligent chatbots, or just bots, have become critical in all industries including the financial industry. Companies are integrating chatbots on their websites, mobile applications, and social media to engage users and perform simple to complex tasks. Alibaba Cloud's Intelligent Robot powers 24/7 customer service. It is able to integrate APIs to do complex tasks such as user registration, tracking transactions, or initiating transactions. Wells Fargo has adopted an AI chatbot on Facebook Messenger to provide accounts and authentication support.
Machine learning algorithms are the only tool capable of identifying and stopping large-scale fraud. Fraud-detection is an urgent problem for the financial sector owing to the growing volume of transactions, user base, and open APIs. Machine learning algorithms can flag suspicious transactions and stop such activity within sub-seconds enhancing the security of the network. Critical IT infrastructure in use today such as government, defense, and, of course, financial transactions rely on advanced machine learning algorithms to beat fraud. A good case study here would be PayPal.
PayPal is the largest online payment processor, and as such, scammers target users in millions every single day. To respond to these threats, three types of threat analysis algorithms have been deployed including linear, neural networks, and deep learning. The three models are deployed simultaneously to effectively combat risks. Such algorithms require massive amounts of data to support their complex training. But PayPal is not under-resourced with data as it collects users' network, machine, financial, and location information.
The use of sophisticated algorithms enables PayPal to separate trustworthy customers from risky customers and expedite their transactions. For risky groups, however, transactions will be slowed down as the system does in-depth analysis of behavior. Suspect behavior will instruct the system to request additional information such as a card number. Such interventions help stop millions of fraudulent transactions on PayPal with precision every day and protect genuine customers.
Financial advisory is another key transformation resulting from machine learning and Big Data. Companies can get a 360-degree view of their customers such as the devices they use, what they buy, where they buy, their location, and thoughts from social media activity. Banks can then collect and analyze all these data points to offer custom financial advisory in the following areas:
Alibaba Cloud's financial AI solution, Financial Brain is a comprehensive tool for deploying financial capabilities in four areas:
Financial Brain has been successfully implemented by several financial institutions. Union Life has deployed an automated customer service platform for an online application using Financial Brain to automate 88% of customer service requirements, authorize leading, and improve customer satisfaction.
In this article, we have looked at some of the transformations that have resulted from Big Data in the financial services sector. As technology continues to advance, Big Data will become even more intertwined with the financial industry to drive innovation and improve customer experience. We are likely to see intelligent bots performing complex roles such as interviewing potential creditors, authorizing loans, intervening on behalf of users, sending routine advice, and even more autonomous tasks.
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