Banking in 2029: A FinTech Executive's Guide to Machine Learning
By Sheri McQueen | June 17, 2019
The power of technology to transform processes and operations, and drive more intelligent business insights, is a game-changer. Banking is no exception: investment in machine learning in banking is expected to explode in the coming decade, with the total business value of AI in the financial sector expected to reach $300 billion over the next 10 years.*
Machine learning – a subset of AI – in which computers use algorithms and statistical modelling to perform key tasks without specific instructions, has the potential to give FinTech organizations an edge over the competition, helping to grow customer loyalty and reduce business risks. But training the algorithms requires data (and vast amounts of it) in order to attain intelligent results. Luckily for banks, the immense amount of data generated via onboarding forms, client logins, online banking sessions and transaction records makes banking ripe for disruption with machine learning when fueled by this content.
Here are four key ways machine learning is transforming the banking sector – and four key reasons banks can’t afford to delay leveraging document classification using machine learning in order to reap the full business benefit of their data.
1. Transform Customer Experiences
By imitating human psychology and intelligence, machine learning can seamlessly automate repetitive tasks with speed and efficiency. And, when paired with RPA, banks can leverage this more cognitive form of process automation to deliver better and faster customer experiences. Take for example the volumes of credit applications banks receive online each day. Machine learning can be utilized to scan the incoming forms, and analyze and extract the key values required to approve each submission, and then feed them downstream to a rules-based RPA system for processing – resulting in an ability to satisfy thousands of customers at once, around the clock.
Machine learning can further fuel better customer service and experiences by helping banks to glean insights from broad customer data sets, helping to intelligently segment clients and deliver tailored service and products based on needs clients may not even know they have. Additionally, as machine learning allows virtual assistants and chatbots to adjust their behaviour and interact more like humans, such innovation can improve the delivery of digital customer service without straining resources.
2. Risk Management
Given that customers essentially hand over the keys to their net worth to their banks, security is paramount to maintaining trust and loyalty.
And from the better identification of vulnerable data across networks and data stores, to smarter analysis of login activity to quickly spot unauthorized attempts to access customer accounts, machine learning can greatly help to protect a client’s vital assets.
For starters, machine learning in banking can help organizations to find and contain risky data such as personally identifiable information (PII), an attractive target for cybercriminals, so that it can be appropriately protected. Machine learning can also aid in the detection of other security risks such as unauthorized attempts to access client information or accounts. This is based on computers’ ability to use past data to determine what “normal” behaviour looks like – for example, during a customer login – and their increasing sophistication in applying this knowledge to efficiently flag scenarios that fall outside of expected behaviour.
3. Fraud Identification & Protection
Not all security risks come from external sources – sometimes it’s the clients themselves looking to perpetuate fraud. From money laundering to fake loan applications, banks are an obvious target for financial fraud. But machine learning in banking provides an accurate and efficient tool for parsing vast amounts of data to identify potentially fraudulent activity.
Take money laundering as an example: in such cases, fraud is concealed within a complex network of shell corporations and transactions that can make it almost impossible for a human to connect the dots. But machine learning capabilities can process massive amounts of data contained within articles of incorporation, transaction data, third-party documentation and other sources to better reveal anything suspicious.
4. Enhance Operational Efficiency
The ability of machine learning to drive intelligent process automation using RPA with unstructured data can drive substantial efficiencies and cost improvements: one report found that as machine learning and AI proliferate, a bank can expect savings of 20 to 25 percent across its IT operations, including infrastructure, maintenance and development costs.** Additional savings also come from the automation of customer service, data entry and other functions, while an improved ability to personalize and tailor offers and services (and even to automate these functions using RPA with unstructured data) can grow revenue and increase the value of each customer.
The logic behind these cost improvements is straightforward:
How can you get started leveraging machine learning to realize the above benefits sooner than later? Because machine learning relies on clean, utilizable data for all of the above functions, learning how to improve the accessibility of your information so that it is machine-consumable is a key first step. Fortunately, machine learning technology can assist in this regard too, helping you to efficiently classify documents and enrich your data to drive competitive value.
Click below to learn how Adlib leverages document classification using machine learning as a solution to delivering high-quality data.
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