Language Matters : Interesting Facts about few Terms

"Bankers" -   This word originates from the old Italian word banco, meaning table or bench, and refers to the fact that financiers once huddled in courtyards to conduct transactions with each other on those tables, in a tangible — relationship-based — manner.

"Payments" - It comes from the French word "Paier" which is used to mean appease.

"Corporation" : Comes from Latin word "Corpus" (body).

"Company"  - Comes from Latin word "companio" which means people breaking bread together.

"Technology" - Word comes from Greek Techne  which translates as skill, art or craft (such as weaving) combined with logos.

DBT(Direct Benefit Transfer) in INDIA through Blockchain

Governments since antiquity have tried to offer some level of welfare to the poor and these programs have expanded in modern times to truly large proportions. For instance, the supplemental nutrition assistance program (SNAP) in America costs approximately $71 billion a year (In 2016-17, GoI DBT payout was approx. 7.54 billion). Reducing the cost of administering these programs and increasing their effectiveness (by better targeting, eliminating fraud and corruption, reducing the number of intermediaries) has been a key challenge for all governments.

The use of technology has improved benefit administration considerably. We have gone from expensive paper tokens (US food stamps printed at the mint to minimize fraud) to electronic transfers straight to the beneficiaries’ bank account (India’s Direct Benefit Transfer scheme).

India’s implementation of Direct Benefit Transfer (DBT) has been a big step in eliminating a lot of the waste of earlier forms of benefit distribution. It not only reduces administration cost but by leaning on Aadhaar, the DBT system reduces both types of fraud – of ‘duplicates’ (a name getting benefits more than once) and of ‘fakes’ (benefits being taken in the name of a non-existent or fictitious person). These features alone make DBT far superior to other forms of benefit transfer.

DBT – Scope For Improvement

Despite the tremendous strides by DBT in eliminating waste, the system suffers from three important drawbacks.

1. Cash Flow Issues For Beneficiaries

While DBT has reduced the need for intermediaries, it has added cash flow burden for the end-beneficiary. So, for instance, in PAHAL (DBT for LPG), the customer has to first purchase the LPG cylinder and then the rebate is remitted to the bank account. This creates a cash flow burden for the beneficiary, which the Government has realized and is addressing through a new scheme – Ujjwala. However, creating new schemes creates new layers of awareness building and administration requirements.

2. Reliance on the Banking Network

Bill Gates famously said: “Banking is necessary. Banks are not.” However, the current DBT system has banks baked into its core as the system cannot work if the beneficiary does not have a bank account. The success of schemes like Jan Dhan Yojana not withstanding, it is worth asking if this reliance on banks is a good thing.

We would argue that it is not. There are real costs to using the banking infrastructure and it creates opportunities for banks to profit at the expense of poor customers. While not an issue yet in India, this has been a major challenge in the United States – where banks have been charging customers to even query their balance.

Furthermore, the cost of operating a bank account is estimated to be $5 per annum. Hence, the 310 million new accounts of the Jan Dhan Yojana cost about $1.5 billion to operate annually. Banks already finding ‘novel’ ways of paying for this cost – for example, by reintroducing the penalty for non-maintenance of minimum balance in its savings accounts. State Bank of India Chief Arundhati Bhattacharya said the public sector lender needs funds to balance the operational costs of Jan Dhan accounts.

3. Lack of Ongoing Transparency

For certain schemes, verification and transparency remain a challenge. For instance, schools are required to admit a certain number of backward and disadvantaged students. However, according to one study, seats are left empty owing to multiple inefficiencies: burden placed on parents, long enrollment process, schools disinterest in enrolling such students, and debt accrued owing to missing refunds. Furthermore, auditing and monitoring of these student enrollments in itself is a burdensome process for the government.

Lastly, one of the findings of the policy is that there needs to be constant monitoring to ensure outcome-based benefit transfer to align incentives of all stakeholders.

Similar verification and monitoring challenges exist across other welfare schemes as well.

A Blockchain-Based PAHAL System

PAHAL has been a hugely successful DBT program. However, currently, the beneficiary is required to pay for the cylinder and then the subsidy is paid to her bank account. She then needs to go to an ATM to access her funds.

The diagram below shows the current system in operation.

In a blockchain-based system, this delay in paying subsidy and need to visit the ATM would be eliminated. The diagram below shows how the system would work:

The token is a digital code that will be sent to the end consumer’s mobile phone number. This digital code will be tagged to the Aadhaar number using a mathematical hash function, thereby making every digital code unique to the Aadhaar number. The reconciliation is automated using the blockchain technology as both the government agency issuing the tokens and the oil companies redeeming the tokens are using the same distributed ledger.

The diagram clearly shows how the redesigned model using tokens is an improvement owing to simplification compared to the current as is model. In summary, the benefits are as follows:

1. Multiple banking transactions (from the government to consumers) are replaced by a handful of transactions (from the government to oil companies).
2. Tokens are tagged to individual Aadhaar numbers, hence reconciliation is exponentially simplified.
3. All transactions – issuance and redemption of tokens – are stored using the blockchain technology, thereby providing transparency and audibility.

The same concept can be expanded to any DBT system. Exact token features and blockchain features will differ from case to case, but at the heart of it, they remain the same.

Governments like the UK are already experimenting with blockchain-based benefit transfer. In India, due to Aadhaar, we have the opportunity to be a leader in the blockchain space as well. It’s time to bring benefit payments to the 21st century and for India to lead that implementation.

EU Banks Are Using AI as a Tool for Transformation

Similar to the largest US institutions, European banks have a strong interest in exploring the impact of AI on business functions. The US market, however, seems to be far more active in areas beyond virtual assistants and chatbots, while European banks are heavily focused on customer-facing interfaces. In fact, a study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, India) found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor.

Banks are using chatbots and voice bots to interact with customers and resolve requests before a human intervention is required. Fortunately, the technology behind it – natural language processing and generation – will make it increasingly difficult for customers to tell whether they are talking to a human or an AI interface. Biometrics, particularly voice and face, could be used as authentication methods to ensure secure interactions.

What else are European banks doing with AI?

Virtual assistants, chatbots

To mention a few examples – Russia-based Tochka Bank has launched a Facebook bot for a range of financial services that include allowing the bank’s clients to check their accounts, finding nearby ATMs using geolocation, calling the bank function, contacting customer support, and making payments via Facebook messenger.

Another major institution has integrated IBM Watson and has designed a customer service chatbot – a natural language processing AI bot to answer customer’s questions and perform simple banking tasks like money transfers. If the bot is unable to find the answer it will pass a customer over to a member of staff.

A London-based bank has deployed a text-based chatbot, which customers can use on the banks’ online help pages. The chatbot can answer 200+ basic banking queries.

Automation

With RPA expected to have a $6.7 trillion global economic impact and a global market potential standing at $8.75 billion by 2024, most institutions in the US have work planned to bring automation across functions. Europe also has its examples – a securities services function for a major institution in Europe is employing a trade-matching tool using artificial intelligence and predictive analysis to further automate the trade processing services it provides to investment managers.

Using predictive analysis, the solution analyzes historical data to identify patterns in trades that have required manual intervention in the past and proactively warn clients and their brokers on their live trading activity so they can take action promptly. The bank is already making good progress, having reached around 98% prediction accuracy.

Compliance, Fraud Prevention, AML

A tool developed by one of the largest European institutions systematically screens contracts for compliance purposes. It takes 15 seconds to screen 150 pages, and the tool makes it possible to identify the names of legal entities, people, locations, vessels, etc.

HSBC, for example, is bringing in robots to help it spot money laundering, fraud, and terrorist funding. The bank is planning to integrate the AI software of Quantexa, a UK-based startup, to screen the vast amounts of data it holds on customers and their transactions against publicly available data, in the search for suspicious activity.

The same institution is also using AI to predict how customers might redeem their credit card points so it can market its rewards offerings more actively and effectively. The rewards program will read customer data to predict how they might redeem their credit card points so it can market the offerings of a certain category – travel, merchandise, gift cards or cash – more actively.

Another European bank has partnered with a startup and implemented its enterprise analytics solution using AI to better identify instances of fraud while reducing false positives. Through this, the bank reduced 60% of false positives and increased true positives by 50%.

ING Bank went ahead with replacement of a traditional rules-based anomaly detection system with one powered by machine learning algorithms. Previous testing has shown this will improve performance significantly – much more than the 5%-10% typically attained in a technology upgrade.

Trading & Investments

One of the largest European banks launched an artificial intelligence bond trading tool that will help human traders to swiftly gather better bond prices. The tool will use data from hundreds of thousands of trades to help the bank’s traders to get better bond prices faster. In a six-month trial, the AI tool led to faster pricing decisions for 90% of trades and cut trading costs by 25%.

UBS, for example, is working on solutions using machine learning to develop new strategies for trading volatility on behalf of clients. It scans vast amounts of trading data and creates a strategy based on learning from market patterns. The strategy, however, has then to be approved by human employees. The bank is also developing an AI tool for investment research. It can screen through market data through SEC filings and can actually do a company valuation with all of the inputs that a human analyst would use and can produce text in a fairly decent quality and almost human-mimicking language.

Deutsche Bank has rolled out new AI-based equities algorithmic platform in APAC, which was designed with a self-learning mechanism allowing its systems to predict equities pricing and volume with more accuracy, thereby enhancing the quality of execution. This was added as a capability to its Autobahn platform.

“Artificial intelligence and machine learning are emerging as the most defining tech-marvel in this new wave of financial services. The technology, along with the abundance of data, has given way to several innovative FinTech business models. Several promising players now use AI to solve some of the major problems for customers in the banking and financial services industry.” – How Banks Are Using AI as a Tool for Transformation

CHAT POT - Architecture