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5 Must-Join Facebook Pages For Data Science, Machine Learning And Artificial Intelligence In 2019

January 3, 2019 at 7:57 AM

While LinkedIn has traditionally been thought of as the business or work focussed social platform, Facebook has been making headway into gaining market share in the space as well. With company pages and groups, Facebook is catering to every interest and aspiration that people might have – and combining that with their social interactions and news sources. Facebook aims to give users a one-stop-shop experience, and it’s very good at doing it.

In recent years, we've seen more groups created that focus on topics we're interested in: machine learning, data science and even AI. These groups share knowledge, tips, best practice and even memes! It's a great community to be a part of if you're interested in the industry, so we've put together our top 5 Facebook groups. Be sure to join these groups soon, as you can expect a lot to happen in these fields in 2019!

Our 5 Favourite Machine Learning, Data Science and AI Facebook Groups

Data Mining / Machine Learning / Artificial Intelligence

Public group

Description: This group is for people who have a general interest in various aspects of data mining, machine learning, human-computer interaction and artificial intelligence. The group is not for people from science background only :). Everyone that abide by the rules is welcome to join, discuss, share and gain knowledge.

The field of AI and related disciplines have grown tremendously in last decades, even though it has been around for quite some time. And has now given birth to an entire industry for finding hidden/unknown information in the data pools in various fields like computer vision, imaging, physics, medicine, astronomy, online data & social media, business intelligence, our own personal data collection of music, movies, images; and so on

And in the coming decades, we are likely creating even more powerful artificial intelligence systems and also new ways for human-computer interactions. And hence it’s essential to understand the implications of it for society.

Share data and share knowledge!!

Members: 84 531

Popular topics: Machine learning, artificial intelligence, data mining, deep learning, data science, big data, Google AI

Find the group here:

Machine Learning, Artificial Intelligence and Data Analytics

Public group

Members: 38 439

Favourite topics: Machine learning, data analytics, AI

Find the group here:

Strong Artificial Intelligence

Closed group

Description: Welcome! Feel free to post links related to AI and to invite AI people you know. The purpose of this Facebook group is to exchange information and ideas about the creation of super-intelligent machines. Members are encouraged to post links and thoughts about cognitive neuroscience, computational neuroscience, evolution, advanced mathematics, artificial intelligence, computational intelligence, robotics, electronics, computer science, computer programming, nanotechnology, philosophy, transhumanism, etc.

A.I. critics are not allowed here and will be eliminated from this group. Discussing whether creating super-intelligent machines is ethical or not is a profound waste of time. We are here because we want to build such super-intelligences. In this group, everyone agrees that Strong A.I. is both possible and desirable because only through Strong A.I. will we be able to reach the high-hanging fruits on the tree of knowledge. Creating Strong A.I. is essential in making the Singularity happen: no Strong A.I., no Singularity.

Moreover, Strong A.I. is the ultimate weapon we will use to defeat ageing and death once and for all. Each day many people die from ageing. Constructing Strong A.I. soon will avoid the suffering of billions of people.

Members: 41 194

Favourite topics: cognitive neuroscience, computational neuroscience, evolution, advanced mathematics, artificial intelligence, computational intelligence, robotics, electronics, computer science, computer programming, nanotechnology, philosophy, transhumanism

Find the group here:

Data Science with R

Closed group

Description: We're just a bunch of data science enthusiasts who like R & Python. Discussion related to analytics, machine learning, deep learning, R, python is most welcome. Please be civil and polite.

In learning you will teach; in teaching, you will learn!

Members: 65 123

Find the group here:

Big Data, Data Science, Data Mining & Statistics

Public group

Description: This is a forum for Big Data, Data Science, Data Mining and Statistics. No ads, no spam, no commercial posts, no free webinars.

Members: 18 855

Favourite topics: Big Data, Data Science, Data Mining, Statistics

Find the group here: 

collection of data science and machine learning resources

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The 7 types of credit risk in SME lending

  It is common knowledge in the industry that the credit risk assessment of a consumer applying for credit is far less complex than that of a business that is applying for credit. Why is this the case? Simply put, consumers are usually very similar in their requirements and risks (homogenous) whilst businesses have far more varying risk elements (heterogenous). In this blog we will look at all the different risk elements within a business (here SME) credit application. These are: Risk of proprietors Risk of business Reason for loan Financial ratios Size of loan Risk industry Risk of region Before we delve into this list, it is worth noting that all of these factors need to be deployable as assessment tools within your originations system so it is key that you ensure your system can manage them. If you are on the look out for a loans origination system, then look no further than Principa’s AppSmart. If you are looking for a decision engine to manage your scorecards, policy rules and terms of business then take a look at our DecisionSmart business rules engine. AppSmart and DecisionSmart are part of Principa’s FinSmart Universe allowing for effective credit management across the customer life-cycle.  The different risk elements within a business credit application 1) Risk of proprietors For smaller organisations the risk of the business is inextricably linked to the financial well-being of the proprietors. How small is small? The rule of thumb is companies with up to two to three proprietors should have their proprietors assessed for risk too. This fits in with the SME segment. What data should be looked at? Generally in countries with mature credit bureaux, credit data is looked at including the score (there is normally a score cut-off) and then negative information such as the existence of judgements or defaults; these are typically used within policy rules. Those businesses with proprietors with excessive numbers of “negatives” may be disqualified from the loan application. Some credit bureaux offer a score of an individual based on the performance of all the businesses with which they are associated. This can also be useful in the credit risk assessment process. Another innovation being adopted internationally is the use of psychometrics in credit evaluation of the proprietors. To find out more about adopting credit scoring, read our blog on how to adopt credit scoring.   2) Risk of business The risk of the business should be managed through both scores and policy rules. Lenders will look at information such as the age of company, the experience of directors and the size of company etc. within a score. Alternatively, many lenders utilise the business score offered by credit bureaux. These scores are typically not as strong as consumer scores as the underlying data is limited and sometimes problematic. For example, large successful organisations may have judgements registered against their name which, unlike for consumers, is not necessarily a direct indication of the inability to service debt.   3) Reason for loan The reason for a loan is used more widely in business lending as opposed to unsecured consumer lending. Venture capital, working capital, invoice discounting and bridging finance are just some of many types of loan/facilities available and lenders need to equip themselves with the ability to manage each of these customer types whether it is within originations or collections. Prudent lenders venturing into the SME space for the first time often focus on one or two of these loan types and then expand later – as the operational implication for each type of loan is complex. 4) Financial ratios Financial ratios are core to commercial credit risk assessment. The main challenge here is to ensure that reliable financials are available from the customer. Small businesses may not be audited and thus the financials may be less trustworthy.   Financial ratios can be divided into four categories: Profitability Leverage Coverage Liquidity Profitability can be further divided into margin ratios and return ratios. Lenders are frequently interested in gross profit margins; this is normally explicit on the income statement. The EBIDTA margin and operating profit margins are also used as well as return ratios such as return on assets, return on equity and risk-adjusted-returns. Leverage ratios are useful to lenders as they reflect the portion of the business that is financed by debt. Lower leverage ratios indicate stability. Leverage ratios assessed often incorporate debt-to-asset, debt-to-equity and asset-to-equity. Coverage ratios indicate the coverage that income or assets provide for the servicing of debt or interest expenses. The higher the coverage ratio the better it is for the lender. Coverage ratios are worked out considering the loan/facility that is being applied for. Finally, liquidity ratios indicate the ability for a company to convert its assets into cash. There are a variety of ratios used here. The current ratio is simply the ratio of assets to liabilities. The quick ratio is the ability for the business to pay its current debts off with readily available assets. The higher the liquidity ratios the better. Ratios are used both within credit scorecards as well as within policy rules. You can read more about these ratios here. 5) Size of loan When assessing credit risk for a consumer, the risk of the consumer does not normally change with the change of loan amount or facility (subject to the consumer passing affordability criteria). With business loans, loan amounts can range quite dramatically, and the risk of the applicant is normally tied to the loan amount requested. The loan/facility amount will of course change the ratios (mentioned in the last section) which could affect a positive/negative outcome. The outcome of the loan application is usually directly linked to a loan amount and any marked change to this loan amount would change the risk profile of the application.   6) Risk of industry The risk of an industry in which the SME operates can have a strong deterministic relationship with the entity being able to service the debt. Some lenders use this and those who do not normally identify this as a missing element in their risk assessment process. The identification of industry is always important. If you are in manufacturing, but your clients are the mines, then you are perhaps better identified as operating in mining as opposed to manufacturing. Most lenders who assess industry, will periodically rule out certain industries and perhaps also incorporate industry within their scorecard. Others take a more scientific approach. In the graph below the performance of an industry is tracked for two years and then projected over the next 6 months; this is then compared to the country’s GDP. As the industry appears to track above the projected GDP, a positive outlook is given to this applicant and this may affect them favourably in the credit application.                   7) Risk of Region   The last area of assessment is risk of region. Of the seven, this one is used the least. Here businesses,  either on book or on the bureau, are assessed against their geo-code. Each geo-code is clustered, and the projected outlook is given as positive, static or negative. As with industry this can be used within the assessment process as a policy rule or within a scorecard.   Bringing the seven risk categories together in a risk assessment These seven risk assessment categories are all important in the risk assessment process. How you bring it all together is critical. If you would like to discuss your SME evaluation challenges or find out more about what we offer in credit management software (like AppSmart and DecisionSmart), get in touch with us here.

Collections Resilience post COVID-19 - part 2

Principa Decisions (Pty) L

Collections Resilience post COVID-19

Principa Decisions (Pty) L