Central Bank Malta

Other areas of possible

application of

BIG Data

and

Machine Learning

technologies

at the CBM

Carlo Camilleri

Statistics Department June 2019

The views expressed in this presentation are those of the author and do not necessarily reflect those of the Central Bank of Malta. Any errors are the author’s own.

BIG Data, Machine Learning & AI at CBM Central Bank of Malta Carlo Camilleri 2

Big Data for CBM - and the need to be proactive

• Big data work still on an

exploratory mode, yet there is

an increased interest

• Key objective for Central

Banks is to better understand

– The new data-sets and related

methodologies

– The value added in comparison

with “traditional” statistics

• Focus on pilot projects


Chatbot Help Desk Automation

• Helpdesk service Chatbots are Virtual Assistants

which can be deployed to improve respondents

service and safeguard the reputational image.

• Chatbots can handle a high volume of requests with

similar responses at 97% accuracy.

• Can be integrated into an organisation website and

other services platforms.

• Examples of Chatbots

• Clare.AI used by online banking services

• NanoRep deployed in logistics eg FedEx and Vodafone

• Twyla works within enterprise systems such are ERPs and

CRMs



• Helpdesk requests are usually ‘basic’ or ‘simple’ questions that take a lot of time to

answer.

• An average question, like a respondent asking about the status of his registration

process on one of our Statistical platforms, usually takes a lot of time. Asking for a

respondent’s Entity Leicode, name of individual, verifying the respondent’s

ID, looking up the Entity code in the system and finally providing the answer, will

take minutes while the respondent is waiting.

• By automating these simple questions using chatbot technology, the same action will

take less time and will take less resources.

• Since the process is much faster this way, the respondent does not have to wait and

will have a more positive experience.



• When questions get more complex and the chatbot is not able to answer, questions will be transferred to a real person who then takes over to service the respondent.

• Chatbot technologies contain self learning technology (Machine Learning) that will improve the performance of your automated helpdesk over time.

• The chatbot will learn which answers are good and which answers need to be improved and uses this learning information for a next request.

• As a result simple questions are handled fast and fully automated, more difficult and complex questions will get the right attention from the helpdesk staff because of the time saved by using chatbot technologies. Better service means happier respondents.


Help Desk BOTS and their Benefits

• Example of a BOT conversing with a human during a

support call.

• The software responds to the questions using a library

of questions and pre-configured answers by using

Artificial Intelligence.


Chatbot Help Desk Automation Wrap-up

• BOTs free up time of your critical resources as they can stay focused on work that

adds value, rather than answering repetitive questions.

• It increases employee engagement and productivity as useful information is just a

chat away.

• Respondents only want to get the information they need without unnecessary

hassle. AI chatbots can perfectly meet respondents’ expectations of how helpdesk

service should operate.

• Once people get comfortable

using chatbots, there will be a

huge demand for them.

Widespread adoption will take

hold.


Other Areas of possible ML and AI applications at CBM

• Examples of areas of possible AI application at CBM, which is by no means exhaustive:-

– Market research is supported by adopting web mining techniques and machine learning in

content analysis, topic modelling and clustering of relevant articles.

– International Asset Management Office (IAMO)

Price developments in bonds / equities / currencies / precious metals over time to discover

trade patterns – algorithmic trading (short- / medium- / long-term)

Market reactions on interest rate decisions from Central Banks (predictability and possible

trade patterns)

– Market Analysis Office (MAO)

Price developments and correlations in various asset classes to better construct a robust and

better Strategic Asset Allocation (SAA) / Tactical Asset Allocation TAA exercises (lower risk

or higher yield/return)

– Government Securities Office (GSO)

Price developments in Malta Government Stocks over time to discover trade patterns –

algorithmic trading (long- / medium- / long-term)


Other Areas of possible ML and AI applications at CBM

• More examples of areas of possible AI application at CBM:-

– In Risk management, neural networks assess and evaluate the financial soundness of the

markets.

– In Statistics, machine learning enables new methods for data quality management, eg in the

context of securities holdings or the classification of company data.

– For our Statistics Technical User Help Desk, the handling of routine requests via automated

chatbot responses could be a useful support measure.

– Social media data can be used to detect trends, turning points or sentiments. Machine learning

methods can be applied for variable selection purposes in econometric models. (Applying an

algorithm to some data, the end result would be a trained model which can be used on new data

or situations with some expectation of accuracy)


Selected Big Data projects by Central Banks

– During the International Workshop on Big Data for Central Bank Policies held in 2018, a number

of Big Data project areas examples were outlined :-


Selected Big Data projects by Central Banks cont…

– During the International Workshop on Big Data for Central Bank Policies held in 2018, a number

of Big Data project areas examples were outlined :-


AI in Finance : Six warnings from a Central Banker

A speech about AI and Banking by Prof. Joachim Wuermeling, Member of the

Executive Board Deutsche Bundesbank, held at the 2nd Annual FinTech Conference in

Brussels :-

• 1 Don’t miss out on the opportunities of Artificial Intelligence in finance …

– Human shortcomings in dealing with finance can be mitigated. As behavioral finance has taught

us, biases, inertia and ignorance lead to the malfunctioning of markets. AI allows humans to

reach out beyond their intellectual limits or simply avoid mistakes.



• 2 … but beware of the risks

– Pattern recognition has its limits which can be dangerous particularly in crisis scenarios.

– Example:- An autopilot would never have been able to land a plane on the Hudson River. Nor

can algorithms stabilise in periods of financial stress.

• 3 Consumers should take care: they remain the risk-takers

– Society has barely begun to understand the economic, ethical and social implications of AI

• 4 FinTechs should not ignore the legitimate concerns of society and supervisors

– The wellbeing of society depends on rules. The public demands cybersecurity, data privacy,

consumer protection and financial stability. FinTechs should not brush aside the concerns of their

stakeholders. Business can only flourish if it is broadly accepted by citizens.

• 5 Artificial Intelligence needs new forms of supervision

– Have effective control environments and appropriate processes for due diligence, risk assessment

and ongoing monitoring of any operations outsourced to a third party.



• 6 Central banks should embrace Artificial Intelligence

– Central banks have access to huge amounts of very valuable data stemming from market

operations, supervision, payments and statistics. They are well positioned to tap the benefits

of AI so they can enhance their ability to fulfil their mandate for price stability and the stability

of the financial system.


Conclusion

• Decisions on data have become of strategic importance for Central Banks.

• Big Data is likely to become a topic of increasing interest to Central Banks in the years ahead. This is because it is likely to change both the internal operations of Central Banks, and transform the external economic and financial systems, Central Banks analyse.

• The new BIG Data approach could involve a shift in tack from analysing structured, aggregated data, to analysing data that is more heterogeneous, granular and complete.

• Bigger and better data could enhance the Bank’s analytical toolkit and improve its operational efficiency, with the end goal being to promote the good of the people of Malta by maintaining monetary and financial stability.

• AI will give the opportunity to turn all that data into knowledge.

& Q A

Thanks for your attention

BIG Data, Machine Learning & AI at CBM Central Bank of Malta Carlo Camilleri 16 16

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