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System Audit with help of Artificial Intelligence in Banking

Across the globe, banks are facing major issue with digital banking, cyber security, regulatory audit and compliance issues. Further, any lapses found during internal / external or regulatory audit of the bank would lead to regulatory fines levied based on the issues ascertained.  The reputation of Bank is founded on trust from its employees, clients, shareholders, regulators and from the public in general. 

Every bank would like to have a robust system, which can help them to identify the issues in advance and report them real-time or periodic basis for further investigations and mitigate the risk. This will help bank to address the regulatory and compliance issues, which will have huge impact on enormous fines from the financial authorities.

Highlighting key system audit issues faced by the bank globally for your reference.

- Data integrity issues (KYC, AML) to know the accuracy and authenticity of data collected by the Banks, FinTech companies and regulators. This has huge impact on customer identification and transaction processing with relevance to remittance and International payments.

- Integration or upgradation of legacy system with inconsistent data to newly migrated applications. This leads to provide dummy data to meet the new system requirements and internally will have impact in reporting due to inconsistent data.

- Failure to maintain Data warehouse solution (DWH). As few banks and financial institutions fail to maintain DWH solution as per regulatory requirements provided by the central banks. This will have impact on performance issue and data extraction for reporting of historical data from the existing systems.

- Failure to maintain reconciliation of General Ledgers / Suspense Accounts for at regular intervals. This will have impact on financial reporting for the FY period. Recon teams with traditional ETL tools do much of this reconciliation work manually with help of spreadsheets.  The major impact on reconciliation involves new data sources, unstructured data, new regulatory changes and huge volume of data, which makes reconciliation process complex in nature. Considering the amount of detailed information in the statement, error in the report can very badly affect the bank’s reputation image.

- Exception Ignorance, User while processing the transaction ignore or bypasses the information provided by the system and workflows prescribed.  This can be done without authorization, elevate their privileges or access level. This will lead to ignore critical information provide to user and they misuse the application according to their needs.

 

Let us see how banks and financial institutions overcome all these system audit issue with latest technologies.

Artificial Intelligence:

          Artificial Intelligence (AI) is fast evolving as the go-to technology for major Banking and financial institutions across the world to personalize experience for customers / SME and corporates. Banks use AI in the following areas to address the major problems faces by them.

  • AI and Machine learning – KYC / AML Checks: Bank can have real impact on KYC compliance process in helping identify high-risk customers or blacklisted customer who need to be screened with an Enhanced Due Diligence (EDD) process. Based on pattern recognition techniques coupled with unstructured text analysis and systems are made more efficient to identify relevant customers for EDD. These methods are crucial and critical in assisting human investigators in   complex webs of evidence and drawing conclusions that are not apparent from any single source of information. 

 

  • BOTS are developed and trained to meet the data validation check as part of migration process or legacy system upgradation to new system. Based on predefined algorithm bots can validate with existing data, arrive at the required data, and supplement the same as part of migration / upgradation. This will reduce the error of reporting wrong data while preparing any regulatory reporting or MIS reports.

 

  • Cloud based Data Warehouse are the emerging trends in storage of huge volume of data. Banks in traditional method maintain separate EDW solution with additional physical server capacity, which involves high maintenance cost and infrastructure for running the same. With help of cloud based DWH solution, bank can use this as services, which has more advantages like lower upfront cost, improved scalability, ready to get deployable solution and performance.  Moreover with help of  AI / ML bank can reap the benefits with  data on cloud like                                                                                        
  •    Rapid pace of innovation in area of reporting and data extracts ,
  •    Easy access to up-to-date data.
  •    Zero Duplication of data.
  •    Scalability & high Performance.
  •    Available 24 * 7.
  • Reconciliation of General Ledger / accounts are major activities to be mandatory performed by bank to have checks and balances on the system. AI / ML plays a key vital role as part of automating reconciliation process. They helps in identifying the pattern provided in the algorithm to check various scan pattern on various data sources and data could be of both structured / unstructured data, identify the issues, and provide the auditable recon report as expected the bank audit team. The accuracy and speed is improvised as part of ongoing process with changes in existing algorithms. More over data control, rules and validation checks are built to avoid the existing issues in coming future.
  • Bank can automate critical process with help of Robotic Process Automation (RPA) cognitive bots and create workflow models. As and when user tries to override or ignore exceptions, system ensure workflow are triggered as per preconfigured setup for the respective processes. Banks needs to identify critical process and implement RPA.  

 

With so many numbers of advantages, banks must consider AI / ML / RPA     as the technology for change. They can provide the edge over competition with reduced cost and improved efficiency for significant growth in business. Only through digitization of system,  bank can have control over regulatory fines and charges levied by the authorities and provide better services to its stakeholders.

 

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Comments: (4)

Shailendra Malik
Shailendra Malik - DBS Bank - Singapore 04 April, 2019, 10:16Be the first to give this comment the thumbs up 0 likes

Why Cloud-based Datawarehouse. The whole movement is to adopt streaming datasets or even no-sql data sets approach on cloud. Putting data warehouse on Cloud means putting heavy ambassador body on a powerful V10 engine which works best with a carbon fiber body to perform at its best speed. The approach you mentioned doesn't make sense from real-time surveillance performance perspective.

Arunachalam N
Arunachalam N - Mindtree - Bangalore 04 April, 2019, 13:50Be the first to give this comment the thumbs up 0 likes

Perfectly agree shailendar. In term of mainteance cost of in house DWH solution is expensive in nature compared to cloud DWH solution.

 

More over  Cloud-based data warehouse architectures can typically perform complex analytical queries much faster because they use massively parallel processing MPP.

Shailendra Malik
Shailendra Malik - DBS Bank - Singapore 05 April, 2019, 04:061 like 1 like

Why I need the limitations of DWH when I can put through an OLAP solution in the cloud to sort with issues my data and build my compute with spark or java to manage the parallel processing to cater scalability challenges. 

If the intention is not to disrupt or minimally disrupt the codebase by lifting and shifting the DWH from on-prem to the Cloud and continue with the legacy codebase then you're not solving the problem but just deflecting it for a later day.

I would advise companies not to fall in this trap and break away from the legacy mentality and break down your code to actually adopt API & microservices approach where ever possible. Start small but create a rolling down effect.

Arunachalam N
Arunachalam N - Mindtree - Bangalore 05 April, 2019, 05:24Be the first to give this comment the thumbs up 0 likes

Agreed

Arunachalam N

Arunachalam N

Senior consultant

Mindtree

Member since

30 Jul 2018

Location

Bangalore

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This post is from a series of posts in the group:

Banking Strategy, Digital and Transformation

Latest thinking in respect to Banking Strategy, Digital and Transformation. Harnessing our collective wisdom to make banking better. Ambrish Parmar


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