Overview
When considering the unprecedented times that we live in, digital transformation has become more crucial than ever. It is imperative for banks as well as legacy business systems to adapt and become more modernised. Simultaneously, due to developments in Artificial Intelligence (AI) and Machine Learning (ML), both have become important enablers to ensure that both hassle- and risk-free digital transformations are conducted. The financial industry is experiencing increasing pressure for risk management, growth governance, and regulatory requirements to become more efficient. It is therefore imperative for entities such as banks to enhance their services to deliver both exceptional and improved customer services. Fintech brands are making increased use of both Artificial Intelligence and machine learning in a variety of applications. These are used across multiple channels to serve customer needs and tailor services according to those needs. You might also be interested in an Ultimate Guide to Big Data.
How Machine Learning applies in Finance and Banking
Banks and other financial institutions can leverage the power offered by AI and Machine Learning along with data science acceleration to enhance the portfolio offerings of customers through the following applications.
1. The mitigation of risk management
This is one of the best examples of how machine learning can be applied in both banking and finance. Banks used to rely heavily on the history of the client to gather an understanding of the creditworthiness of a respective customer. This process was not always accurate and there were often difficulties in approving loans. Through digital transformations and improvements through Artificial Intelligence and machine learning, the machine learning algorithm analyses the customer in an improved manner. This has increased the efficiency of loan approvals in addition to the accuracy of verifying information regarding the customer.
2. Protection from fraudulent activities
Banks form part of some of the highest regulated institutions. This requires for banks to comply with strict government regulations pertaining to default prevention and avoiding financial crimes being committed on systems. This is one of the main reasons why banking processes have become entirely digital in a short span of time. In order to effectively mitigate fraudulent activity, it is imperative to understand the risk before suspicious activities can begin. Machine learning can be used to sense suspicious activities before there is external violation of the customer’s account. This means that machines can perform high- and in-depth level analysis in real-time without delay.
3. The use of Chatbots
Chatbot technology is heavily based on software led by AI which has the ability to clone human conversation. The technology behind chatbots makes it convenient for banks to serve customers’ queries more efficiently. This cuts down on time and effort spent handling individual customer queries by employing chatbots to handle large scale customer issues.
4. Marketing based on algorithms
The benefits derived from being able to recognise past behaviour of each customer and to develop targeted campaigns applies to both customers as well as banks. The customised campaigns create the information which pertains specifically to the customer which suits their needs and preferences. In turn, this also enhances the customer’s experience. For more information visit OQLIS’ website here: https://www.oqlis.com/
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