Aseel Investment Company is a crowd-investing platform licensed by the Capital Market Authority. It adopts the crowd-investing model in investment funds and seeks to achieve feasible returns by investing in investment opportunities. Established at the beginning of 2022, Aseel has achieved great accomplishments in the field of financial technology – FINTECH – where it set an unprecedented record in Saudi Arabia and the Middle East. Using Oracle technology, Aseel launched an investment fund serving over 5,000 investors in under four minutes, raising over 90 million riyals (US$24M), and other funds worth 155 million riyals (over US$41M), which closed in under 10 minutes.

As a market-leading investment company, Aseel management knows that leveraging AI and machine learning can provide numerous benefits to help them maintain their position as a market leader. With this vision, Aseel started to work with Oracle data science experts on use cases and build them on the Oracle Cloud platform.

One of the high value use cases is calculating investor risks and predicting whether investors will invest in the future again – be repeat investors. One-time investors provide a surge of capital that can be used to fund new projects and expand Aseel’s operations. Yet, it is important to convert those customers into repeat investors for sustainable growth.

By accurately assessing investor risks, Aseel strives to allocate its resources effectively and focus attention on potential high-value investors, develop targeted marketing strategies, and optimize efforts to attract and retain investors who are more likely to invest multiple times.

Now, with machine learning models, Aseel can:

  • Predict future re-investment potential of one-time investors: Repeat investors contribute a more continuous revenue stream for the investment company.
  • Increase customer satisfaction by offering the right funds at the right time: Repeat investors are more likely to increase their investments over time since they build trust with their investment company. By actively engaging with investors who have multiple investment potential, Aseel can build longer-term relationships with their customers.
  • Manage the customer journey starting from Aseel platform registration to long term customer conversion: Knowing customer’s investment preferences increases the likelihood of customers continuing their journey with Aseel – fostering trust and increasing satisfaction.

Our pilot project allowed us to validate the feasibility and viability of our machine learning use case. It helped us determine if the chosen machine learning algorithms, data, and techniques are suitable for solving the problem at hand.

With the power of Oracle Autonomous Database’s built-in machine learning capabilities—Oracle Machine Learning—the Oracle and Aseel data science teams prototyped this use case. They achieved over 73% accuracy to predict which of the one-time investors have the potential to become a repeat investor.

Oracle Machine Learning Notebooks helped with data preparation for machine learning modeling using the power of Autonomous Database. For the modeling phase, OML AutoML UI, a component of Oracle Machine Learning, sped up algorithm selection, feature selection, and model tuning to complete our innovation prototype and see the technical feasibility, viability, and business desirability within 4 weeks. Finally, registering OML models in Oracle Analytics Cloud, business users and C-level management can now see the predicted repeat investors with prediction probabilities to take appropriate business actions.

“As a pioneering investment company, we at Aseel see huge opportunity in applying Oracle Machine Learning and Autonomous Database to revolutionize our business model. With Oracle as our trusted partner, we have started a transformative journey, collaborating with Oracle data science experts to unlock the art of the possible.”
Ali Al Shamrani, Chief Operating Officer, Aseel Investment Company

For more information, see resources Oracle Machine Learning and Oracle Autonomous Database.