My Thoughts on Data-Driven Decisions in Modern Banking
7 years in business development have given me the privilege of working with one of the European banks backed by financial capital from the United Arab Emirates. I could get a backstage view of the lending process, which allowed me to observe the challenges borrowers face and the innovative solutions my team masterfully provided at that time.?
Back in 2017, long before the COVID-19 pandemic, there was already a need for inclusive and embedded financing. What COVID-19 did was accelerate the modernization of banking processes, pushing many banking and non-banking companies towards positive changes in customer success, financial product sales, and development. In simpler terms, it catalyzed the digitalization (and personalization) of financial and banking services.
In an era of abundant consumer choices, financial institutions must not only focus on personalization but also understand how scaling personalization can transform their entire business model. Financial services can take cues from other industries, like streaming services, which have adeptly adapted to consumer preferences.
An extract from the PWC report: "Digital intelligence: Choose-your-own-adventure model in financial services."
Clients no longer want to hear from brokers cold-calling using yellow pages. They don't need banking services advertised on billboards.?
Here's the thing: from the get-go, every client—be it an individual or an SME—expects a tailored commercial offer that resonates with their needs.
Whether you're gleaning insights from website cookies, conducting client surveys, or diving deep into customer preferences based on their credit product choices, it's imperative that these insights are rooted in robust client data. Relationship banking is becoming everything for bank and non-bank lenders in the upcoming decades.
In this digital era, the data you gather on client behavior isn't just information—it's a goldmine. It's the key to transforming your offerings into lucrative opportunities. After all, in the world of business, data truly is the new gold!
In this video, I'll walk you through the essential steps to refine and optimize your data set, ensuring you extract the most valuable business insights. ??
My insights from working with AI
GiniMachine is a no-code AI-powered decision-making platform designed to provide bank and non-bank lenders, as well as debt collection agencies, with actionable insights and predictive models. You can simply upload data and GiniMachine will automatically process and analyze it, generating precise predictive models tailored to your specific business needs.?
领英推荐
Do you know why I adore working at?GiniMachine AI? The software truly transforms the mindset of a lending business globally. I have the unique opportunity to see how our clients and partners are redefining their understanding of lending and the value proposition behind each of their credit offers. Each project targets a specific audience and aims to address the unique pain points of each borrower.?
How is this possible with a vast client base and multiple credit products? The answer lies in segmentation combined with value-based loan offers. By segmenting clients based on credit preferences, region of purchase, and borrower type, patterns and unique behaviors of each group become evident. Use this knowledge for upselling or cross-selling your credit products. Retain your clients by understanding their needs, challenges, and how to address these challenges with the financial support of your bank or non-bank lending company.
Problem solving with AI. Credit company example
A credit company from the APAC region had a client base of over 300,000 individual borrowers. They faced a challenge: a 10% increase in the default rate on loans over an operational year, which could lead to potential losses of around a hundred thousand dollars. Here's how GiniMachine turned potential losses into valuable insights and helped reduce business expenses:
The insights we discovered with GCredit (this is what we call the GiniMachine features for credit companies) were invaluable. Our team found that borrowers in the trade and transport sectors experienced a notably higher volume of defaults. After a cross-analysis, it became evident that crises within these sectors had a direct impact on borrowers. Factors like delayed salaries or workforce reductions hindered their ability to repay loans on time. Consequently, the average loan repayment duration for these borrowers extended by 10-15 days, leading some credit contracts into default or delinquent payment status.
With the new AI-driven decision-making mechanism, lenders can now see the financial capabilities of each borrower in detail and make counter-offers to mitigate risks, either by reducing the loan amount or shortening the loan term. Lenders continue to grow their credit portfolio quantitatively while reducing credit risks through segment-based lending. That’s because the integration of GCredit with the lender's LOS/LMS helped maintain the speed of evaluating each borrower, even with an increased pipeline for borrower assessment.
Final thoughts
Data-driven decision-making is not the future of financial business, it’s a day-to-day reality. If you still rely on old school analytics, you’re holding yourself back from efficiency.?
At GiniMachine, we understand that it may be difficult to switch to new tools. That’s why we offer a 21-day free trial for financial business to try GiniMachine out and experience a brand-new level of analytics and decision-making. During this trial period, you'll have full access to all our features, allowing you to see firsthand how GiniMachine can upgrade your operations.
To get started, simply contact me or visit our website https://ginimachine.com/