Account Opening: How to Efficiently Acquire Target Customers
This is the third and final installment of a blog series that explores friction — friction that affects both customers and the banks themselves — across the account opening process.
Digital transformation. Ever-changing customer preferences. Increased competition. To navigate successfully in today’s landscape, banks are well-aware that they must account for all these developments, efficiently meet critical goals, and target new customers to survive. But fraud schemes and attacks are growing increasingly complex, to the point where they make it difficult for banks to achieve what they set out to do. Finding a means to easily, efficiently, and effectively manage fraud and minimize friction can be easier said than done, but luckily, there are ways to do so. In this post, we’ll cover the capabilities of account opening solutions, including machine learning (ML), that help troubleshoot these challenges without sacrificing banks’ objectives.
For many banks, customer acquisition, boosting top-line revenue, and increasing operational efficiency are all tied to their digital strategies. Stopping fraud cannot come at the expense of these critical objectives — even as fraud risk continues to evolve in complexity.
Additionally, stopping fraud also shouldn’t raise costs. According to Neustar, “not being able to authenticate good customers upfront also increases costs…each review costs the institution about $10. Multiply that cost by thousands of applications, and profits can take a significant hit.”
What can banks do to stay competitive?
Effectively leverage real-time machine learning during account opening
There’s a reason why the big buzz is around machine learning. For one, ML makes a night-and-day difference when it comes to account opening and scoring “good” applicants in milliseconds. It’s also known for unlocking new possibilities to improve customer experiences.
The advantages of effectively leveraging real-time machine learning also benefit banks themselves. In today’s world, there are ML tools that help increase operational efficiency for banks in two ways:
- drive down manual reviews and increase auto-approvals of target customers;
- enable fraud analysts with clear insights to make faster and more accurate decisions when conducting manual reviews at scale.
Decrease manual reviews to increase operational efficiency
Having rules in conjunction with machine learning models helps increase auto-acceptance rates and acquire more target customers. The backend automation reduces costs and the length of time for manual reviews, which, consequently, boosts operational efficiency.
Advanced data science tools equip data scientists with the means to choose how they want to work and automate tasks quickly and conveniently. For instance, by using the right tool, they can leverage the existing expertise and processes (e.g., languages and platforms of their choice) that they are most comfortable with to build accurate models. On the other hand, tedious components of the data science workflow (such as data exploration, feature engineering, and model training, etc.) can be automated, allowing data scientists to deploy models in a fraction of the time it would take.
For what you can’t auto-accept, better tools enable better decisions
For those applicants that can’t be auto-accepted, having the right tools and capabilities in place increases operational efficiency for fraud analysts. By arming them with more insight and knowledge about which customers to acquire, they’re able to improve risk assessment.
Some of the best tools are explainable AI and link analysis.
Fraud analysts need to understand the underlying reasons for a high-risk score. Explainable AI provides them with clear, easy-to-understand “human-readable” explanations, giving them greater insight to make correct decisions in the moment.
Furthermore, a visual link analysis of all available data attributes enables fraud analysts to efficiently identify suspect applicants and common entities that aren’t visible through traditional review methods. It leverages powerful AI technology that helps fraud analysts see if any transaction clusters look “off,” allowing them to quickly spot fraud patterns and relationships that are hard to uncover.
Making the Most of Data
Equipping data scientists with the right tools to build accurate models and deploy them faster is essential. However, advanced data science tools are only as useful as the data they have access to. Data enrichment is an invaluable capability used to improve risk scoring of applicants. Banks should adopt a solution that maximizes the value of new and existing third-party partnerships, which boosts their chances of increasing the acquisition of legitimate customers. Leveraging third-party services for ID verification, IP geolocation, device ID, email intelligence, and other services allows for a complete picture of applicants, enabling fraud analysts to make better decisions.
Grow top-line revenue
Ultimately, these tools don’t just reduce friction and increase operational efficiency for banks. They help banks grow. By adopting intelligent tools and capabilities that make it easier to get fuller insights and assess risk, banks can identify and acquire a broader scope of target customers with increased lifetime value. In the long run, gaining and retaining these customers increases banks’ top-line revenues.
Evolving in today’s landscape can be challenging and daunting for banks of all sizes. However, as more and more customers want digital-only experiences, those struggling to adapt to these preferences inadvertently put themselves at risk. By effectively using the right account opening solution powered by machine learning, though, banks can be better equipped to grow into leaders in the era of digital transformation.
Want to explore account opening in more depth? Download our new eBook Account Opening: Fighting Fraud and Friction
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