A brief overview of some of the projects Finserv Experts consultants have worked on…

Lloyds - Standardized

At Lloyd’s of London, Areiel is currently serving as the blockchain solution architect for the publicly acclaimed London Market Target Operating Model programme.  Much of Lloyd’s post-settleent processing remains as paper based as it was when Lloyds becaome the world’s leading insurance market in the early 1700’s.  Areiel’s role is to help the programme envision a solution in which the transaction accounting, claims agreement, net settlement, and reporting functions are digitized and automated via a transparent, immutable, blockchain-based ledger.


ICAEW - Standardized

The ICAEW is the main professional body for accountants in England and Wales; they have asked Areiel to help chart out the impact that the advent of blockchain is going to have on the audit profession.  Many of the current processes followed by auditors may end up being disintermediated, but the fundamental role of audit assurance becomes more necessary than ever in a distributed ledger environment, and Areiel is helping the ICAEW map out how the requried skills and activities that auditors perform will change as a result.


BAML Consortium 2

Areiel worked with Bank of America, HSBC, and the Monetary Authority of Singapore to lead the development of a working blockchain prototype for letter of credit origination.  This was IBM’s very first commercial consulting engagement for blockchain, and successfully delivered on time.


BOT- Standardized

Areiel advised the central bank of Thailand on the creation of a blockchain working group to give blockchain fintechs in Thailand a competitive advantage by supporting them with clear guidance and proactive regulatory engagement.


CBA - Standardized

Areiel worked with Commercial Bank of Africa to lead the development of a machine learning based credit scoring solution for loans issued over mPesa, Kenya’s world leading mobile payments solution.  Over 40% of Kenya’s GDP flows through mPesa, and more than half if its users have no bank accounts, let alone a financial history which could be used as the basis for traditional credit scoring algorithms.  Areiel led a team of developers and data scientists to create a solution which used machine learning to predict the likelihood of default based on the applicant’s payment history and other data about the loan. The pilot of this solution reduced CBA’s non-performing loans by over 26%.


MITRE

Areiel worked with the MITRE corporation to draft a detailed set of interoperability requirements and standards for a large outsourcing contract.