Data Governance In The FinTech Sector: A Growing Need - Pwc

December 2020Data governance inthe FinTech sector: Agrowing need

IntroductionOver the past few years, India has emerged as a centre ofinnovation in the FinTech space. With over 3,800 FinTechs1providing tech-enabled services – with data and analyticsas a key component – to consumers and businesses,consumption of data is increasing exponentially. Data isboth a key input driver as well as a source of differentiationfor the FinTech industry. As more and more adjacentsectors get digitised, there will be an explosion of bothfinancial and non-financial data whose governance shouldbe a top priority for both FinTechs as well as their partnerfinancial services organisations.Beyond technological innovation, the FinTech sector isalso at the forefront of major shifts in regulation. Someof the key data regulations and laws include the EU’sGeneral Data Protection Regulation (GDPR), the CaliforniaConsumer Privacy Act (CCPA) in the US, and India’sPersonal Data Protection (PDP) Bill. For the FinTechindustry and the financial services industry in general, theGDPR presents a unique set of challenges. The industryneeds to establish a robust data governance framework asplayers in the financial services space typically hold andprocess large amounts of wide-ranging customer data as acore part of their business.A data governance (DG) framework covers every partof an organisation’s data management process, dataarchitecture and data models, and extends right down toindividual technologies, databases and data repositories.Some of the widely used DG frameworks are:Global data governance frameworksDAMACMMIDMBOK (DataManagement Body ofKnowledge)(Capability MaturityAssessment Model)ARMAEDM Council-DCAM(InformationGovernance MaturityModel)(Data CapabilityAssessment Model)Secure point of sales enabled lendingPoint of sale (PoS) enabled lending is a model whereindigital lenders partner with other ecosystem players(e-commerce companies, payment gateways, etc.)to finance users’ online transactions by utilising acombination of conventional and unconventional data.With the partnership model at the core, FinTechs needto regulate the usage of data to process loans betweendifferent partners. Key DG domain areas to be consideredinclude data privacy/security and creating appropriatedata-sharing agreements and firewalls between thedepartments to ensure limited data access and sharing.Usage of alternative data sources for lending is expectedto multiply with the introduction of the new Open CreditEnablement Network (OCEN) framework by iSPIRT. Aspart of OCEN, lending companies can utilise flow-baseddata to provide loans to small businesses and individualborrowers. To effectively harvest and use data from varieddata sources, governance controls related to data access,data security and data sharing, etc., would need to beimplemented.A leading private sector lender has built a well-governedand controlled ecosystem to manage its lending process.Key focus areas were the discovery and inventorying ofthe key personal identifiable information (PII) of customers,building of data lineage across all data sources, anddefinition of a new set of access policies and securitycontrols. As a result, the company is now better equippedto support its internal business objectives and fulfilregulatory and compliance requirements.The establishment of a proper data governancemechanism in the FinTech space is particularly relevantwith the advent of and traction gained by new innovativebusiness models. We look at some of the latest trends inthe FinTech space and the need for a strong DG approach:1. -in-india-fintech-2020#: :text %20the%20millenium2 PwC Data governance in the FinTech sector: A growing need

Governing data usage in the gig economy model ininsurance sectorThe insurance sector has traditionally worked withindependent insurance agents, who have played acrucial role in supporting customers in choosing theright coverage for their needs. A number of new ageFinTechs have built upon this model by providing digitalplatforms to manage freelance agents. These platformsuse multiple data-driven capabilities – from creating aholistic technology platform with tools for managing clientrelationships to providing opportunities for scaling up.At the same time, they also provide organisations withinsights into managing the workforce and highlight failurepoints in the agent journey.It would be crucial to implement DG frameworks coveringkey areas such as metadata management, data privacyand security, and data quality in this model. Theseframeworks would ensure regulated usage and sharingof data, maintain quality of data churned, and ensure thatproper controls are implemented to maintain anonymity ofuser data.Nowadays, many InsurTechs have become vigilant aboutsharing data with freelancers. Consent managementtools are being discussed as the privacy expectations ofconsumers cannot be overlooked. And with new privacyregulations coming in, consumer privacy has become aprimary concern before sharing data with anyone.Regulating data usage in partnership-enabledneobanking modelThe neobanking model is another FinTech model thathas seen significant traction globally. In India, neobanksprimarily operate in partnership with one or multiplebanking partner(s). This leads to sharing of data betweenthe two entities for multiple banking services provided toconsumers.To ensure regulated usage and security of customer datashared by banks with neobanks and vice versa, properdata security and access guidelines would need to bein place.3 PwC Data governance in the FinTech sector: A growing needOther FinTech segments, including payments andWealthTech, also require strong DG frameworks to ensurecompliance both within the organisation and across itspartners.In recent times, the industry has seen the introductionof several data-related laws and regulations aimed atensuring the privacy and security of an individual’s PIIand sensitive data. Some of the key focus areas includedata sharing, data usage, consent and an individual’s datarights. Hence, there is increasing pressure on companiesto remain compliant while adopting rapidly evolvingFinTech models.Considering the changing regulatory landscape andrequirements, some FinTech companies have alreadyperformed readiness assessments and have startedto adopt an enterprise DG framework that would helpthem ensure effective data management, be compliantand continue to have opportunities for subsequent datamonetisation.

PwC’s Enterprise Data Governance(EDG) FrameworkPwC’s EDG Framework considers the current and next generation data lifecycle and architecture requirements andupcoming DG challenges.2 This framework can be easily customised as per the data and technology requirements ofFinTech firms.PwC’s EDG FrameworkDG strategyand charterProgramme managementData stewardshipLineageGlossaryAccess and ownershipMaster dataQualityMetadataSecurity and privacyInnovationChange ManagementPolicy, processand operating modelPeople and cultureTools andtechnologyInformation lifecycle ingRetentionDisposalStructured Apps and DBs Master and reference data hub EDW and DL Unstructured2. ustry-frameworks-for-data-governance.html4 PwC Data governance in the FinTech sector: A growing needData modelDG coreareas blockGrowth (monetisation)Productivity Trust Brand equity PerformanceDataarchitectureWhat togovern?Control (efficiency)DG capabilityenablerHow togovern?DGfoundationWhy togovern?Governance strategy, vision and impact

PwC’s EDG Framework answers three important aspects of DG:EDG Framework blocksFoundationEnabler and coreWhat to govern?How to govern? DG strategy and charter: This block identifies thebusiness drivers, vision, mission and principles forDG, including readiness assessment, internal processdiscovery, and current issues or success criteria. DG core areas: It covers core areas which shouldbe prioritised for the successful execution of any DGprogramme. This includes seven key domains and fourinterlocking areas:a. Key domain areas: Business glossary, Metadatacatalogue, data lineage, data quality, master data,data privacy and security and data access andownership. The domain areas can be prioritised asrequired.b. Interlock areas: Stewardship and workflowmanagement, change management, programmemanagement and innovative tech management5 PwC Data governance in the FinTech sector: A growing needStrategy and charterWhy to govern? DG enablers: This block helps in identifying the keycapabilities required to operationalise all the othercomponents of the EDG Framework. These criticalenablers are people and culture, policy, process andoperating model. DG foundation: It covers the data management designand operational functions (modelling, architecture,storage and operations, etc.) that are required tobe implemented to support traditional uses of data(business intelligence and document and contentmanagement).Data trust and capability measurement modelswhich help in demonstrating impact and maturityimprovements across the programme lifecycle areadditional benefits of the EDG Framework.

ConclusionIn the current landscape, where FinTechs continue toleverage ecosystem digitisation strategies, innovativedata and analytics techniques, and multi-organisationpartnerships to grow and provide focused services,management of data and ensuring proper compliancesbecome increasingly important. Adopting a robustenterprise DG framework will enable FinTechs toaccommodate the changing data requirements both froma business and regulatory point of view, and ensure dataprivacy and security. A trusted FinTech data ecosystem isthus essential and the need of the hour.6 PwC Data governance in the FinTech sector: A growing need

About PwCAt PwC, our purpose is to build trust in society and solve important problems. We’re a network of firms in 155 countrieswith over 284,000 people who are committed to delivering quality in assurance, advisory and tax services. PwC refers tothe PwC network and/or one or more of its member firms, each of which is a separate legal entity. Please see www.pwc.com/structure for further details.Find out more about PwC India and tell us what matters to you by visiting us at www.pwc.in.This article has been researched and authored by Ambrish Anand, Sanjana Agarwal, Avneesh Narang andPrakash Suman.Contact usMukesh DeshpandeAmit LundiaVivek BelgaviPartner and Data Management LeaderPartner and Data Governance LeaderPartner and India FinTech Leader 91 98450 95391 91 98369 22881 91 98202 80199PwC Indiamukesh.deshpande@pwc.comPwC Indiaamit.lundia@pwc.comPwC Indiavivek.belgavi@pwc.compwc.inData Classification: DC0 (Public)In this document, PwC refers to PricewaterhouseCoopers Private Limited (a limited liability company in India having Corporate Identity Number or CIN :U74140WB1983PTC036093), which is a member firm of PricewaterhouseCoopers International Limited (PwCIL), each member firm of which is a separate legal entity.This document does not constitute professional advice. The information in this document has been obtained or derived from sources believed byPricewaterhouseCoopers Private Limited (PwCPL) to be reliable but PwCPL does not represent that this information is accurate or complete. Any opinions or estimatescontained in this document represent the judgment of PwCPL at this time and are subject to change without notice. Readers of this publication are advised to seektheir own professional advice before taking any course of action or decision, for which they are entirely responsible, based on the contents of this publication. PwCPLneither accepts or assumes any responsibility or liability to any reader of this publication in respect of the information contained within it or for any decisions readersmay take or decide not to or fail to take. 2020 PricewaterhouseCoopers Private Limited. All rights reserved.KS/December 2020-M&C10068

needs to establish a robust data governance framework as . Partner and Data Management Leader PwC India 91 98450 95391 mukesh.deshpande@pwc.com Amit Lundia . neither accepts or assumes any responsibility or liability to any reader of this publication in respect of the information contained within it or for any decisions readers