Machine learning was developed as part of a multi-industry push for artificial intelligence (AI) driven by emerging technologies like the Internet of Things (IoT). Today, over 76% of businesses (including those in the financial sector) indicate IoT will be critical to future success. Moreover, the financial sector is actively developing and incorporating machine learning into its core competencies and offerings for customers.
Next Level Services for Consumers
Pairing machine learning with IoT can permit novel high-value services for consumers in the financial sector. The examples seem straight out of a science-fiction movie. Imagine returning back home after a difficult day, and realizing that you ran out of milk. Fortunately, your refrigerator is “smart enough” to directly order milk when needed and pay for it directly from your bank account. When you go car shopping, instead of not knowing how much a bank will loan you, imagine receiving an automated personalized message, while you are at the car dealer, which says “Mr. John, save 1,000 euros on that new Mercedes-Benz E-Class Coupe if you use our bank financing.”
Benefits for the Insurance Industry
In the insurance sector, machine learning may lead to more customized payments. For instance, healthcare insurance providers incentivize healthy lifestyles. Individuals that have healthy lifestyles can significantly reduce health insurance costs. On the other hand, those individuals that start developing unhealthy habits will receive a personalized “warning message” from the insurance provider: “Mr. Doe, this is a reminder that the cost of your insurance is based on your health habits and may increase accordingly.” Influencing and driving healthy personal habits of customers can help insurance companies increase profits considerably.
How to Implement Machine Learning and IoT in the Financial Sector
In order to turn these ideas into a reality and successfully implement machine learning applications into the financial sector, organizations will need to focus on developing a flexible technology adoption strategy. This strategy will implement new technologies, including those from other industries, into their core offerings. Insurance companies will monitor health habits and biometrics by integrating wearable devices into their offerings. Banks will be able to provide personalized marketing messages for financing with the support of Beacon, a Bluetooth Low Energy (BLE) technology that can identify the geographical area of specific users. Finally, the technology required to make your refrigerator “smart” and provide you a carefree life where milk magically appears as needed is the same technology being incorporated into next generation smart appliances.
It is obvious that the machine learning era can bring the financial sector to a new level of customization and sophistication for its customers. In order to do this, managing and safeguarding a huge amount of data will be required. Given the sensitive nature of the data held by the financial institutions, one concern for consumers is privacy and the risk of a cyber security incident compromising their data. Fortunately, other technologies will increase safety and transparency for financial institutions. Blockchain has already been introduced as an enabler of secure online transactions that can combine different safety approaches such as tokenization (which replaces the credit card number with a unique payment number) and biometric identification.
Machine learning is one of many breakthrough technologies that will drive the financial sector’s evolution, and the future is bright for those organizations who are able to evolve and incorporate all of these technologies into their business.
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