For #MoneyMatchSpotlight on Data Science, we talked to the Head of our Data team, Ashdave Singh Gehoonia, to find out more about the brains behind some of our core internal data platforms & technologies! Ashdave has an educational background in Mathematics with Actuarial Science, and is an MBA graduate from Hult International Business School. His professional journey started as a Structured Product & Foreign Exchange Trader within the Treasury department of AmBank Sdn. Bhd., with a professional license to trade and deal foreign exchange and money market securities on the Malaysian interbank market. He joined MoneyMatch in 2018 as our first Data Science Manager, tasked with creating, building and integrating a unified data strategy into our internal architecture, platforms, and decision-making processes across the organization.
The Data Team
Hi Ashdave, can you describe the main roles of the Data team at MoneyMatch and how the team operates?
There are 5 distinct roles within the Data Team here at Moneymatch:
- Data Architect: Designs the overall data architecture of the organization. They determine how to collect, store, and process data while aligning data assets with business objectives.
- Data Engineer: Designs and maintains the infrastructure that supports analytics. They ensure effective collection and storage of data, while optimizing data processing pipelines for performance and scalability.
- Data Analyst: Interprets data and generates insights for the business. They use statistical and visualization tools to identify trends, patterns. Analysts then present their findings to stakeholders in a clear and concise manner.
- Business Intelligence Analyst: Creates reports and dashboards that provide key performance indicators (KPIs) and business insights to stakeholders. Data analysts and business leaders work closely with them to ensure that the reports are relevant and actionable.
- Machine Learning Engineer: Designs and implements data models, as well as creates and tests machine learning algorithms. Data analysts and engineers work closely with them to leverage data and create intelligent systems.
We adopt a hybrid approach—mixing in both a centralized structure along with elements of decentralization. We consolidate all data-related functions under one department while assigning a main data scientist to work closely with each department.
As the Head of the MoneyMatch Data team, what are your primary responsibilities?
My primary responsibilities typically include the following:
- Strategy: Defining and executing the overall data strategy for the organization. This includes identifying data-related opportunities, prioritizing and aligning initiatives with the broader business goals.
- Team Management & Technical Leadership: Developing the team’s critical-thinking by hypothesizing outside the box when applying data solutions to business problems. As well as providing a strong understanding of the tools and techniques needed to drive insight.
- Collaboration & Communication: I often have to work with other business units that consume data and analytic insights within the organization. Collaboration with senior management and stakeholders on extracting insights and integrating strategy is also a big part of the job.
How does your team’s work align with the overall goals and objectives of MoneyMatch?
Our first and utmost priority is on customer acquisition and retention. We work with the Sales & Marketing team by analyzing customer data to gain insights into their preferences, behavior, and needs. We further utilize this to develop targeted marketing campaigns, improve product features, and enhance the overall customer experience.
Secondly, is in the field of risk management. We work with the Compliance team to manage risk by analyzing large volumes of data and identifying patterns or anomalies that may indicate fraud or other types of risk. All with the main aim of identifying and responding to potential threats in a timely manner.
The final area we focus on is process efficiency and cost reduction through automating processes and optimizing workflows. We build integrated intelligent systems to reduce the time and cost associated with rote processes and manual decision-making.
What are examples of the core data-driven features or platforms implemented by MoneyMatch to improve internal operational efficiency?
- Corporate Sales Platform
We built the MoneyMatch Corporate Sales Platform (CSP) with core functionalities of a customer relationship management software. It acts as the central hub for sales force automation, aimed at streamlining the sales process flow. The platform is integrated between the Sales and Compliance team’s respective platforms for customer onboarding. This integration allows both teams to reduce the time taken to communicate details and diagnose problems. The CSP has a highly adaptable data analytics software built-in to improve decision-making. This platform hinges on Big Data Analytics tools, is self-serviceable and provides real-time and centralized data management.
- Compliance Analytics Platform
Our Compliance Analytics Platform (CAP) empowers our compliance officers to identify potential threats, and minimize risks and costs associated with lost funds or data breaches. It is built for accuracy at scale and has a comprehensive set of analytical tools tailor-made for the Compliance team. Armed with this actionable intelligence, they are able to monitor transactions in real-time and identify any issues or irregularities. The platform has drastically reduced the time taken for the Compliance team to exercise their daily responsibilities, thus increasing departmental output.
- Proprietary Inventory Management Platform
The MoneyMatch Inventory Management Platform is a custom inventory management and analytics platform. It combines both inventory allocation optimization algorithms and end-to-end integration with the MoneyMatch Transfer platform. The centralized system creates a seamless experience for the Treasury team, with reduced dependency on other third-party software. This platform contains in-house built algorithms to automate operational tasks and decisions. This helps generate more efficient output, data-driven outcomes, and in turn reducing operational errors.
Opportunities & Challenges
How do you prioritize and manage competing demands for limited Data Science resources, both within your team and across the organization?
It’s a continuously challenging task. Careful consideration is given to identify the projects that will have the biggest impact on the business by evaluating their complexity and potential impact. By prioritizing projects with high potential impact and lower complexity, we’re able to optimize the use of limited resources.
Furthermore, it is crucial to involve stakeholders from across the organization in the prioritization process. This is to communicate the inherent trade-offs and ensure that they have a clear understanding of the implications of different project priorities.
Finally, we regularly track, measure, and deliberate our progress on the projects currently being built as well as those upcoming. This is so that we remain transparent and flexible, whilst being able to adjust our priorities as needed to fit the ever-changing needs of our consumers.
How do you stay current with and leverage emerging technologies to improve MoneyMatch’s data management and analysis?
We stay informed by attending conferences, webinars, and other industry events. Besides that, we also make the habit to read relevant publications and research papers to stay up-to-date with the latest developments in the field. Additionally, we continuously evaluate emerging technologies to determine their potential benefits and drawbacks to the organization by conducting research, consulting with experts, and theorizing proofs of concept internally.
We pilot the new technologies once we ascertain the minimum value proposition. We do this by implementing them in a controlled environment to measure their impact on data management and analysis. This structure enables the team to determine whether they are worth scaling up and incorporating into the organization’s processes.
In addition, we collaborate with the IT and Engineering teams to implement new data management tools, as well as the Product teams to integrate new analytic tools into products. Finally, we invest in training to ensure that team members have the skills needed to navigate the technology.
The Evolution & Future of Data
From your perspective, how has Data Science changed over the years?
For the Fintech industry in particular, the role of a Data team has changed significantly and will continue to evolve in the near future. Here are some of the key changes that have taken place rapidly over the recent years:
- Increasing importance of data-driven decision making
Fintech companies have come to rely heavily on data to drive decision-making. This has led to a greater focus on data quality, data governance, and data-driven processes.
- Greater emphasis on automation
As the volume of data continues to grow, Data teams will need to find ways to automate repetitive tasks. Consequently, this will require the use of advanced technologies such as machine learning and AI.
- Shift to real-time data analysis
Real-time data analysis allows for quick response to changes in the market or customer behavior. This requires Data teams to develop new techniques for managing and analyzing real-time data.
- Emphasis on security and compliance
Fintech companies are subject to strict regulatory requirements. Data teams must ensure that the data they manage is secure and compliant, whilst being leveraged on to build robust anomaly detection models and risk mitigation controls.
How do you envision the future of Data Science in the next 5 to 10 years?
Looking ahead to the next 5 to 10 years, the future of Data teams in Fintech companies is likely to be shaped by several key trends, including:
- Greater use of machine learning
As the volume of data has increased, so has the use of machine learning to analyze and make sense of that data. This has and will lead to the development of more advanced models that can generate more accurate and sophisticated predictions.
- Integration with other technologies
Data teams will need to work closely with other teams such as Engineering and Product Development to integrate data analysis with other technologies such as blockchain and IoT.
- Increasing importance of explainability and interpretability
As predictive models become more complex, there will be a greater focus on ensuring that they are easily understood by non-experts.
Let’s look forward to a AI-led, technology-charged and data-driven future! Stay tuned for our upcoming interviews in the #MoneyMatchSpotlight series.