Data and Analytics: Defining a Vision and Delivering to a Roadm
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Data and Analytics: Defining a Vision and Delivering to a Roadm

Mahesh Bharadhwaj, Head of New Borrower Analytics, Funding Circle
Mahesh Bharadhwaj, Head of New Borrower Analytics, Funding Circle

Mahesh Bharadhwaj, Head of New Borrower Analytics, Funding Circle

Small businesses power the global economy. They drive 50% of GDP, 60% of private-sector employment, and make up 99% of all firms. However, despite the wealth of benefits they provide, they have historically struggled to receive the financing they need to thrive

Traditional banking solutions to SME lending drove poor customer outcomes because they could not fluidly and flexibly account for the diversity of small business types and the wideranging and complex risks they face. This, coupled with the significant credit exposure small business lending requires (when compared to consumer lending) tended to drive overly limited, cumbersome, and lengthy lending processes, filled with paperwork, back-and-forth, and disappointment.

Funding Circle was created to transform this space. We aim to deliver a superior customer experience across the lifecycle for small business finance that is as simple and smooth as consumer finance. To do this, we combine proprietary, cutting-edge analytics, industry-standard platforms, in-house technology, and a wide variety of data sources. This allows us to micro-target our marketing, so customers see the most relevant offers, build powerful risk scores that reduce the need for manual processing and paperwork, utilize lifetime relationship-based valuations to price competitively, and optimize our processes, so the end-to-end application process takes days instead of months. In fact, 40% of our customers now receive an instant offer due to our advanced instant decision technology.

This has been made possible by a confluence of significant advancements in the data analytics space, driven by transformational improvements to the underlying dataprocessing technology. The concept of the 3 V’s is invoked so often that it seems almost a given, but it bears repeating in this context. The first V is Volume. We realize the power that the vast volumes of data now available for storage and processing can contribute to our models and have appropriately invested in the right cloud-based data lakes. This has enabled us to maximize the volume of data we can make available in a fluid manner. We have also moved beyond the standard sources of company and bureau data to source a large Variety of data from multiple sources, including broader access to bank data mandated by government initiatives. This ensures that we can build the most advanced models to deliver a compelling experience to our customers. And finally, the substantial improvements in the Velocity of data processing technologies permit us to build and operate more complex decision algorithms. These can deliver rapid—often instantaneous— decisions to customers, thereby empowering small businesses to concentrate their time and energy on growing their businesses instead of filling out paperwork. This is what allows us to often make funds available to a business, even before their traditional bank has returned their call.

  ​We aim to deliver a superior customer experience across the lifecycle for small business finance that is as simple and smooth as consumer finance. 

These very same technological improvements have also transformed a number of other industries by changing the landscape of the data and analytics space. They have also attracted high-quality talent into the data and analytics space, with multi-skilled data analysts, scientists, and engineers keen to join organizations that have a strong purpose and work on compelling solutions. This drives a virtuous cycle of investment, innovation, and growth.

With great advancement comes great disruption, and thus great challenges. The transformation of the data and analytics space has caused organizations at various stages in the data maturity lifecycle to realize that they also need to invest and upgrade to catch up with their competitors and serve their customers better. However, the sheer pace of change and the explosion in the number of technologies, solutions, and vendors create confusion and uncertainty for many organizations, who often wonder where they should start.

The key realization organizations must come to regarding data and analytics is that the field is not just about the tools, talent, or algorithms, and not about what competitors are or are not doing. The key steps an organization needs to take to have an effective data and analytics strategy are: (1) Define and secure broad-based buy-in on a clear short, medium, and long-term vision for their organization, and set up well-defined milestones along this path (2) Set up the right organizational structure, empowerment, and culture to enable the transformation (3) Only then go forth with hiring and purchasing, in accordance with the vision and the culture. Simply hiring a Chief Data and Analytics Officer and/or buying a solution does not mean you have a data and analytics strategy.

Once a data and analytics infrastructure and the team is in place, it is essential for organizations to also keep abreast of key trends in the space. The latest developments in AI and Machine Learning should be regularly reviewed for how they apply to organizational needs. It is worth taking a broader view of this. While a new development may not immediately be of use to improve pricing or marketing, it could perhaps assist in operational improvements or customer servicing. For example, automated chatbots would not traditionally be front of mind when considering business growth or revenues, but they can be invaluable in reducing the operational load on customer service agents—especially in times of heavy load.

Along with monitoring technological change, it is important to monitor the regulatory, legal, and PR environment in which the data and analytics strategy operates. While GDPR has been front and center for a few years, concerns around how to explain machine learning models and algorithmic bias have recently come to the forefront of public consciousness. Thus, a well thought out viewpoint on data ethics and governance of data use is now becoming essential across this space.

In closing, it is critical that as practitioners, we truly internalize that for all the excitement that models, technology, and data bring, they all exist in service of the needs of the organization and the customer. To each investment in technology or modeling or data, the key questions to ask should be as follows: How will this benefit company revenues/ costs? How will this improve the customer experience? How will this make things more efficient? At Funding Circle, we started on our journey by asking these very questions, and we continue to ask them as we move boldly forward.

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