Banking in the contemporary world cannot be imagined without 2 words- digital disruption and problem loans. Both these are seen as separate matters. Only few realise that they must rather complement each other to create a synergistic effect.

Digital platforms and interfaces can be seen everywhere. These have completely modified the lives of banking staff and clients. Every bank tries to provide data analytics to facilitate customer interaction, revenue generation, enhancing productivity and Customer relationship management.


The operating model for banks is driven by the following factors:

  1. Impact of technology especially open source technology
  2. Analytics tools Eg. Hadoop
  3. Artificial Intelligence systems
  4. Internet of things (IOT)
  5. Intuitive automation

Banks have initiated the digital drive. Investments and retail space are most prominent players. Corporate banking and risk management applications started off late. The most crucial problem in the Indian banking sector is the sluggish adoption of digitisation in problem loan areas. Everyday newspapers talk of the following aspects.

  1. Recapitalisation models
  2. Problem loans
  3. Resolution through IBC and NCLT
  4. Overleveraging

Why is RPA required in banking systems?

Though banks have access to digital platforms, there are many limitations in their functional efficiency. It is believed that adoption of robots will mitigate the lack of transparency. Robotic Process Automation (RPA) can target the deficits in banking sector and improve customer relationship management (CRM). Retail and consumer banking offers will be better communicated to customers.

Sources of errors in banking

The biggest sources of banking errors are their Non Performing Assets(NPA’s).The problems created by borrowers are

  1. Cash misutilization
  2. Non effective project management
  3. Improperly designed corporate structure
  4. Excess leverage

Banks on the other hand cause issues like

  1. Divergent loan appraisal standards
  2. Lack of common database
  3. Dependence on imperfect business models
  4. Lack of regular notification to statutory authorities.

Strategies for problem solving

Solution of such problems needs a rational and modern approach. Possible steps could be
1. Deploying standard open source and analytics tools/Dashboards

  1. Distributing insights
  2. Adopting AI for removing subjective appraisals or prejudiced decisions.
  3. Loan management using Block Chain Technology

Scope for future improvements

Many banks have promptly responded to Early Warning Signals(EWS). They have invested in deployment of Big Data and Analytics for creating dashboards. However the application is manual at first level. Use of Robotic Process Automation with EWS analytics can help take quick steps to counter existing problems. Algorithmic models and analytics can trace patterns of fund pilferage from numerous points in BFSI. The application of RPA and Block Chain Technology will bring transparency, DBMS, reliability and accessibility in banking units.  In future, we may see the adoption of solution structures using Geotagging and NFC.
The inclusion of process enhancement and automated databases will augment efficiency in decision making. Documented analyses will be accessible for validation and audits. So the banker’s fear of proving accountability in a banker will be dissolved. Data Sciences and RPA can play significant roles in framing legislation’s or policy making.

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