Increase Resilience in Banking and FinTechs with AI-driven Good Governance
by Dr. Bikramjit Chaudhari, on Feb 12, 2024 7:19:05 PM
Key takeaways from the blog
- Connected Data and AI-driven good governance enables BFSI to overcome administration and compliance complexities.
- It allows the introduction of new investment opportunities and special-purpose vehicles that can generate new revenue streams.
- Connected Data helps automate administration and compliance to reduce human involvement and create a healthy balance between risk and reward.
Banking, Financial Services, and Insurance (BFSI) sector has multiple regulators that make the space administration driven. The scenario makes the executive more compliance-focused rather than customer service-focused. The customer is always inspired by quick service with minimum clicks and low to no in-person interactions. However, the existing scenario makes the BFSI sector unsettling in most respects, with their major customers staying back only for loans, thus generating only the related cash flow. There is a lack of revenue streams that bring in deposits due to excessive rules, KYC, and compliance-related documentation and tying down of funds for major periods with no scope for early withdrawal without a penalty. Increasing amount of data generated in the process and related data management makes it a chicken-n-egg scenario. Connected Data assisted with AI-powered Customer Experience and Good Governance enables the BFSI executive to address the data conundrum and improve stability and resilience thus reducing the unsettling customer churn.
Institutionalize AI-powered Good Governance in BFSI with Connected Data
Connected Data, unified data, or single-source-of-truth is the master data that is curated and connected with unique customer ids and serves as the source of reference throughout the customer's lifetime journey. This data forms the basis for all real-time analysis as well as financial, operational, and organizational resilience. However, the data quality needs to be ascertained right at inception or during data creation as Value-in is Value-out and so also Garbage-in is Garbage-out. Artificial Intelligence (AI) helps institutionalize good governance by weaving together organizational datasets and bringing harmony in the multi-faceted regulatory and supervisory rigor (which is otherwise tangential to each other along the compliance landscape). Connected Data is the harbinger of proactive 24x7 offsite surveillance to assess risk and vulnerabilities and also setting up of risk mitigation and management mechanisms. Institutionalizing exclusive AI-driven GPT-4 LLM may be cost-prohibitive for SMEs; however, building AI-driven Connected Data frameworks proves cost-efficient in the long run for them.
Connected Data, Data-driven Inferences, and Decision-making
Connected Data aims at proactive and preventive surveillance through data-driven inferences and decision-making. BFSI is a sensitive area that deals with and manages public monies. Especially sensitive are special purpose vehicles (SPVs) that fund joint ventures through joint funds of a consortium consisting of 20 to 30 of entities. Connected Data enables seamless management of such SPV-backed joint ventures, where the program can be easily monitored right from inception to launch and across its lifetime. Data-driven interventions, inferences, and monitoring supports good underwriting standards and post-launch monitoring. AI is a great leveler. It helps detect patterns, mitigate risk, and regularize operations for the long run while fulfilling compliances at all levels. It reduces human-dependent monitoring related to funding of ventures thus making financial operations resilient and allows the human/executive to focus more on customer service. Having said that AI-driven data frameworks should comply with local laws when operating in cross-geography scenarios.
Data and Good Governance Foundation Principles
- Create a RACI blueprint
RACI (responsible-accountable-consulted-informed) matrix is pertinent for institutionalizing data and AI-driven good governance to ensure that the key stakeholders are in the decision-making process.
- Align business goals and business governance
Design analytics and business governance KPIs around business goals. Orienting the data-driven business governance practices around business goals is helpful in measuring and monitoring business outcomes.
- Establish trust
Institutionalize Connected Data that takes into account different lineages of data that helps business owners and stakeholders to make contextually relevant decisions. BFSI is a sensitive space and establishing trust goes a long way in building financial and operational resilience.
- Institutionalize transparency
Establish data and AI-driven governance practices around Connected Data frameworks such that decisions are clear and traceable and can be scaled up as per requirement. Build auditable trails connecting across investments, expenditures, decisions, actions, and compliance.
- Institutionalize risk management
Make risk management a priority and address rewards and risks in the same Connected Data framework. Create a holistic view to balance the outcome between the two extremes in the highly sensitive BFSI space while focusing on long-term business interest and risk appetite.
- Establish succession and training frameworks
Plan a clear implementation and delivery blueprint along with RACI stakeholders. Chart clear measurable goals and training frameworks and RACI succession plans before rolling out the AI-driven good governance practices.
BFSI sector is highly sensitive and monitored by multiple regulations often tangential to each other. It thus occupies the human executive in administration and compliance with no time for a human approach to the real customer issues redressal, which results in blocking out new revenue streams. Connected Data and AI-driven good governance practices enable BFSI organizations to improve financial and operational resilience. Connected Data enables BFSI to introduce new investment opportunities and SPVs that can be seamlessly monitored over their lifetime. It helps automate administration and compliance to reduce human involvement, create a healthy balance between risk and reward and leverage the human effort for customer service. However, complying with local data laws while institutionalizing AI-driven innovations is absolutely paramount.