Personalized, data-driven banking services are becoming the market’s favorite.
More and more banks are migrating their traditional platforms to innovative data-driven applications.
Equipped with large Artificial Intelligence engines, the banks that are ahead are those capable of offering powerful personalization services that directly impact the relationship with their customers, granting more loyalty and a longer life cycle.
But the personalization of products and services is not the only benefit of Artificial Intelligence applied to the financial industry. It is fraud prevention.
Online transactions open a huge universe of possibilities for fraud, being the stone in the shoe for any company based on online purchases, sales or deposits. Financial companies need to guarantee reliability to their customers and, at the same time, avoid billionaire losses of money that is leaked through electronic fraud.
Innovate in the financial industry
Benefit # 1: build customer loyalty
Constant innovation is not a requirement that the market places only on young industries, such as startups. No company is left out of the unstoppable technological and cultural current, which develops and demands products and services tailored to each user.
This phenomenon generates changes in consumption that are transferred to all the companies with which people interact. After doing almost anything from an application: ordering food, managing urban transport, vacations and car insurance, customers increasingly want to solve more things from their cell phone. And so they will demand it from their bank.
Operations that previously took hours and required traveling to a bank branch, today can be carried out from the cell phone or the computer itself, while waiting for the start of a meeting or the departure of the children from school.
In this highly competitive scenario, no bank can be left behind. You need to develop tools that allow customers to resolve all transactions from their mobile device in a practical and reliable way.
If what you are looking for is not to be left out of a market based on continuous improvement, innovating in the financial industry is not an option. It is a must.
Why is it so important to innovate?
The impact of these personalized services and products in the financial industry directly affects the ties of belonging and loyalty that are formed between customers and banks.
However, we also know that betting on innovation has its other side: it demands internal cultural changes and a large investment. There are fears, prejudices and organizational structures that it is essential to understand in order to transform.
The end user, be it an individual customer or a company, becomes more and more demanding. As you assimilate the use of your cell phone to solve everything in your life, you demand that applications work better and better and are available for everything.
On the bank’s side, the requirement to offer a fail-safe service implies a very great commitment and responsibility.
Difficulties in accessing the account, delays and complications to reverse a fraudulent credit card payment or constantly receive offers that do not interest you can lead our loyal customer to close their account, change banks or choose another credit card.
The banks that lead the way in deeply analyzing consumer habits (with machine learning, artificial intelligence and data analysis strategies) tempt our most loyal customers with tailored products and services.
This loyal customer volatility represents a huge challenge for a traditional bank.
On the other hand, it is known that the financial industry has clients with a very long life cycle and a very high acquisition cost. Once the customer changes banks, it takes a long time before he decides to change providers again.
For all these reasons, it is essential to innovate in the financial industry, which is very demanding in terms of continuous improvement, availability and customization.
Benefit # 2: avoid fraud
Fighting fraud has become, in recent years, one of the issues that most disturb financial institutions of all kinds; be they banks, lenders or e-commerce.
Banks of the stature of HSBC, Citibank or Lloyd’s Bank recognize fraud as the main challenge of their management, and demand from them a strong management stance, capable of supporting innovative strategies to face it.
Fraudulent activities are, today, a billionaire industry in constant growth, which is mainly benefiting from the boom in online transactions.
Virtual transactions top the list of cybercrime, facilitated by new technologies.
Fraud can be executed at different times of an online operation: in the user registration, in the purchase and transfer confirmation, or in the duplication of cards, for example.
Fraud detection is not solved with the development of traditional algorithms.
A traditional algorithm that detects a high percentage of fraud is a double-edged sword. The general rules that these systems implement usually find a large number of “false positives”, that is, legitimate cases marked as fraud.
Different and innovative developments must be used to detect fraud accurately and efficiently.
So how do you develop an efficient fraud detection system without sacrificing user experience? Machine Learning is the answer.
With the advancement of the industry based on technology applied to finance and investment (FinTech), the user experience became a very valuable asset for any company that wants to be a leader.
Machine Learning and Artificial Intelligence systems provide solutions for many types of complex problems: they allow you to design recommendation engines, obtain patterns, group customers by similar behaviors and, of course, detect fraud.
Their strength lies in the fact that they become more precise with larger volumes of data, as they “learn” to better generalize the information (a natural characteristic in Fintech companies).
Innovation in the financial industry: success stories
In 2018, by incorporating Machine Learning techniques into day-to-day operations, HSBC reduced fraud false positives by 20% and found numerous behavior patterns directly related to fraudulent practices.
With the improvements in the detection system introduced by HSBC in 2019 (analysis of behavioral biometric data), it reduced false positives by 70%, compared to the same execution in the hands of traditional algorithm systems.
Danske Bank of Denmark (founded in 1871) developed a fraud detection system with traditional techniques that allowed it to detect 40% of fraud; of all of them, 99.5% turned out to be false positives. By incorporating Machine Learning techniques, the entity reduced false positives by 60%, and hopes to achieve 80% after further improvements in the algorithm. Actual fraud detection also increased by 50%.
Using Artificial Intelligence to improve its Sign Up service, CitiBank increased the registration of users by 70%, when with the previous system they would have been denied due to alleged fraud. This also decreased manual false positive verification by 10 times. All of this, without the loss of actual fraud.
What do I need, then, to modernize my bank?
Organizations today have two main goals: ensuring and managing security, on the one hand, and creating great user experiences, on the other. To achieve these objectives, more and more companies make use of Artificial Intelligence.
By understanding customer behavior across omnichannels and omnidata (large data scale) an organization can reduce risk and increase revenue, all in one go.
Now, how could a startup face an Artificial Intelligence project, like the ones we are proposing, without being shipwrecked along the way?
In our post “MVM: Minimum Viable Model: How to take advantage of data science in your startup?” We will tell you how you can develop a data-based product and apply Machine Learning to your business in projects of any size, in an iterative and incremental way.
The development of agile data models allows specialized professionals to work side by side with those responsible for fraud detection. Going through phases of definition, validation and with a defined scope, a personalized fraud detection model is developed, adjusted to the characteristics and needs of the financial institution.
At ScrapingPros we have the expertise to transform any challenge into added value for your financial institution.
Our team of engineers, analysts, developers, data scientists and project managers generate solutions that go from low to high in an iterative and incremental way.
